pygsti.report
¶
pyGSTi Reporting Python Package
Subpackages¶
Submodules¶
pygsti.report.autotitle
pygsti.report.cell
pygsti.report.colormaps
pygsti.report.convert
pygsti.report.factory
pygsti.report.figure
pygsti.report.fogidiagram
pygsti.report.formatter
pygsti.report.formatters
pygsti.report.html
pygsti.report.latex
pygsti.report.merge_helpers
pygsti.report.modelfunction
pygsti.report.mpl_colormaps
pygsti.report.notebook
pygsti.report.notebookcell
pygsti.report.parse_notebook_text
pygsti.report.plothelpers
pygsti.report.plotly_plot_ex
pygsti.report.python
pygsti.report.report
pygsti.report.reportableqty
pygsti.report.reportables
pygsti.report.row
pygsti.report.table
pygsti.report.textblock
pygsti.report.vbplot
pygsti.report.workspace
pygsti.report.workspaceplots
pygsti.report.workspacetables
pygsti.report.workspacetexts
Package Contents¶
Classes¶
Python representation of an IPython notebook 

The internal model of a report. 

A computed quantity and possibly its error bars, primarily for use in reports. 

An ordered set of labeled matrices/vectors. 

A basis that is the direct sum of one or more "component" bases. 

A label used to identify a gate, circuit layer, or (sub)circuit. 

Gate eigenvalues 

Circuit eigenvalues 

1/2 diamond norm of difference between productA(circuit) and productB(circuit) 

Half the diamond distance bewteen model_a.operations[op_label] and model_b.operations[op_label] 

Central to data analysis, Workspace objects facilitate the building of reports and dashboards. 

Encapsulates a set of gate, state preparation, and POVM effect operations. 

Base class for defining a state space (Hilbert or HilbertSchmidt space). 

A quantum circuit. 

A unmutable list (a tuple) of 

Encapsulates a set of circuits, along with an associated structure. 

A label used to identify a gate, circuit layer, or (sub)circuit. 

Class responsible for logging things to stdout or a file. 
Functions¶





Just adds special processing with f0 is a dict, where we 

Evaluate a ModelFunction object using confidence region information 

SPAM dot products (concatenates POVMS) 

Choi matrix 

Choi matrix eigenvalues 

Trace of the Choi matrix 

Eigenvalues of dot(productB(circuit)^1, productA(circuit)) 

Frobenius distance btwn productA(circuit) and productB(circuit) 

Entanglement infidelity btwn productA(circuit) and productB(circuit) 

Average gate infidelity between productA(circuit) and productB(circuit). 

Jamiolkowski trace distance between productA(circuit) and productB(circuit) 

Nonunitary entanglement infidelity between productA(circuit) and productB(circuit) 

Nonunitary average gate infidelity between productA(circuit) and productB(circuit). 

Eigenvalue entanglement infidelity between productA(circuit) and productB(circuit). 

Eigenvalue average gate infidelity between productA(circuit) and productB(circuit). 

Eigenvalue nonunitary entanglement infidelity between productA(circuit) and productB(circuit). 

Eigenvalue nonunitary average gate infidelity between productA(circuit) and productB(circuit). 

Eigenvalue diamond distance between productA(circuit) and productB(circuit). 

Eigenvalue nonunitary diamond distance between productA(circuit) and productB(circuit). 

POVM entanglement infidelity between model_a and model_b. 

POVM Jamiolkowski trace distance between model_a and model_b 

Half the POVM diamond distance between model_a and model_b. 

DEPRECATED: Decompose a 1Q gate into rotations about axes. 

Upper bound on entanglement fidelity 

Jamiolkowski state of closest unitary to gate 

Fidelity between gate and its closest unitary 

Jamiolkowski trace distance between gate and its closest unitary 

Array of angles between the rotation axes of the gates of model. 

Entanglement fidelity between a and b 

Entanglement infidelity between a and b 

Entanglement infidelity between closest unitaries to a and b 

Frobenius distance between a and b 

Jamiolkowski trace distance between a and b 

a gaugeinvariant quantity that behaves like the unitarity 

a gaugeinvariant quantity that behaves like the unitarity 

Returns (d^2  1)/d^2 * (1  sqrt(U)), where U is the unitarity of a*b^{1} 

Returns (d  1)/d * (1  sqrt(U)), where U is the unitarity of a*b^{1} 

Returns (d^2  1)/d^2 * (1  sqrt(U)), where U is the eigenvalueunitarity of a*b^{1} 

Returns (d  1)/d * (1  sqrt(U)), where U is the eigenvalueunitarity of a*b^{1} 

Eigenvalue entanglement infidelity between a and b 

Eigenvalue average gate infidelity between a and b 

Eigenvalue diamond distance between a and b 

Eigenvalue nonunitary diamond distance between a and b 

Returns the average gate infidelity between a and b, where b is the "target" operation. 

Angle between the rotation axes of a and b (1qubit gates) 

Eigenvalues of b^{1} * a 

Eigenvalues of log(b^{1} * a) 

Eigenvalues of log(a * b^{1}) 

Eigenvalues of log(a)  log(b) 

Eigenvalues of b^{1} * a 

Project errgen on all of the standard sets of error generators. 

Projections of log(b^{1}*a). 

Projections of log(a*b^{1}). 

Projections of log(a)log(b). 

Projections of log(A*B^{1}) using a gaugerobust technique. 

Decomposition of gates in model_a using those in model_b as their targets. 

Average model infidelity 

Prediction of RB number based on estimated (A) and target (B) models 

State fidelity between state vectors a and b 

State infidelity fidelity between state vectors a and b 

Trace distance between state vectors a and b 

State vector as a standard density matrix 

Eigenvalues of the density matrix corresponding to a state vector 

Returns a nice humanreadable name and tooltip for a given gatefunction abbreviation. 

Evaluates that gatefunction named by the abbreviation name. 

Infidelity between instruments a and b 

The diamond norm distance between instruments a and b. 

Evaluates that instrumentfunction named by the abbreviation name. 

Decorator for deprecating a function. 

Simple routine to add currentlabels to a list of 

Like _add_new_labels but perform robustsuffix processing. 



Creates a zip file containing the a directory ("offline") of files need to display "offline" reports. 



Creates the "master switchboard" used by several of the reports 

Constructs a dictionary of idle tomography results, parallel 



DEPRECATED: use pygsti.report.create_standard_report(...) 

Create a "standard" GST report, containing details about each estimate in results individually. 

Creates a report designed to display results containing for nqubit noisy model estimates. 

Create a "report notebook". 

Returns the standard Clifford compilation for model, if one exists. Otherwise returns None. 

Create a "standard" GST report, containing details about each estimate in results individually. 

Creates a report designed to display results containing for nqubit noisy model estimates. 

Creates a Drift report. 

Creates an empty volumetric plot with just the axes set. 



Creates a volumetric benchmarking plot. 

Creates a volumetric benchmarking boundary plot, that displays boundary at which the given data 

Creates a capability regions plot from a VBDataFrame. Default options creates plots like those shown 

Creates volumetric benchmarking plots that display the maximum, mean and minimum of a given figureofmerit (by 
Attributes¶
 class pygsti.report.Notebook(cells=None, notebook_text_files=None)¶
Bases:
object
Python representation of an IPython notebook
 Parameters
cells (list, optional) – List of
NotebookCell
objects.notebook_text_files (list, optional) – List of filenames (text files with ‘@@markdown’ or ‘@@code’ designating cells).
 DefaultTemplate¶
The default template notebook to use (a .ipynb file).
 Type
str
 DefaultTemplate = Empty.ipynb¶
 to_json_dict(self, template_filename=DefaultTemplate)¶
Using an existing (usually empty) notebook as a template, generate the json for a new notebook.
 Parameters
template_filename (str, optional) – Name of an existing notebook file to build from
 Returns
dict
 save_to(self, output_filename, template_filename=DefaultTemplate)¶
Save this class to a file as a jupyter notebook
 Parameters
output_filename (str) – File to save the output jupyter notebook to
template_filename (str, optional) – Name of an existing notebook file to build from
 Returns
None
 add(self, cell)¶
Add a cell to the notebook
 Parameters
cell (NotebookCell object) – Cell to add.
 Returns
None
 add_block(self, block, cell_type)¶
Add a block to the notebook
 Parameters
block (str) – block of either code or markdown
cell_type (str) – tag for the cell. Either ‘code’ or ‘markdown’
 Returns
None
 add_file(self, filename, cell_type)¶
Read in a cell block from a file
 Parameters
filename (str) – filename containing either code or markdown
cell_type (str) – tag for the cell. Either ‘code’ or ‘markdown’
 Returns
None
 add_code(self, block)¶
Add code to notebook
 Parameters
block (str) – Block of python code
 Returns
None
 add_markdown(self, block)¶
Add markdown to notebook
 Parameters
block (str) – Block of markdown (or HTML)
 Returns
None
 add_code_file(self, filename)¶
Add a code file to the notebook
 Parameters
filename (str) – name of file containing python code
 Returns
None
 add_markdown_file(self, filename)¶
Add a markdown file to the notebook
 Parameters
filename (str) – name of file containing markdown
 Returns
None
 add_notebook_text(self, text)¶
Add custom formatted text to the notebook.
Text contains both python and markdown, with cells differentiated by @@code and @@markdown tags. At least one cell tag must be present for the file to be correctly parsed
 Parameters
text (str) – notebook formatted text
 Returns
None
 add_notebook_text_file(self, filename)¶
Add a custom formatted text file to the notebook.
Text file contains both python and markdown, with cells differentiated by @@code and @@markdown tags. At least one cell tag must be present for the file to be correctly parsed
 Parameters
filename (str) – name of file containing notebook formatted text
 Returns
None
 add_notebook_text_files(self, filenames)¶
Add multiple notebook text files to the notebook, in order
 Parameters
filenames (list(str)) – names of file containing notebook formatted text
 Returns
None
 add_notebook_file(self, filename)¶
Append an .ipynb file to this notebook
 Parameters
filename (str) – ipynb file to append
 Returns
None
 add_notebook_files(self, filenames)¶
Add multiple notebook files to the notebook, in order
 Parameters
filenames (list(str)) – names of file containing ipynb json
 Returns
None
 launch_new(self, output_filename, template_filename=DefaultTemplate)¶
Save and then launch this notebook with a new Jupyter server.
Note that this function waits to return until the notebook server exists, and so is difficult to work with.
 Parameters
output_filename (str) – filename to save this notebook to
template_filename (str, optional) – filename to build this notebook from (see save_to)
 Returns
None
 launch(self, output_filename, template_filename=DefaultTemplate, port='auto')¶
Save and then launch this notebook
 Parameters
output_filename (str) – filename to save this notebook to
template_filename (str, optional) – filename to build this notebook from (see save_to)
port (int, optional) – Port to launch server on.
 Returns
None
 class pygsti.report.Report(templates, results, sections, flags, global_qtys, report_params, build_defaults=None, pdf_available=True, workspace=None)¶
The internal model of a report.
This class should never be instantiated directly. Instead, users should use the appropriate factory method in pygsti.report.factory.
 Parameters
templates (dict (str > Pathlike)) – A map of the available report generation types (html, pdf, notebook) to template paths.
results (Results or similar) – The underlying Resultslike object used to generate this report.
sections (iterable of Section) – Collection of sections to be built into the generated report.
flags (set of str) – Set of flags controlling aspects of report generation.
global_qtys (dict (str > any)) – Keyvalue map of report quantities not tied to any specific section.
report_params (dict (str > any)) – Keyvalue map of report quantities used when building sections.
build_defaults (dict (str > any), optional) – Default values for the build_options parameter of this instance’s build methods. Defaults to an empty dict.
pdf_available (bool, optional) –
True
if the underlying results can be represented as a static PDF. If this report cannot be represented statically,write_pdf
will raise. Defaults toTrue
.workspace (Workspace, optional) – A
Workspace
used for caching figure computation. By default, a new workspace will be used.
 _build(self, build_options=None)¶
Render all sections to a map of report elements for templating
 write_html(self, path, auto_open=False, link_to=None, connected=False, build_options=None, brevity=0, precision=None, resizable=True, autosize='initial', single_file=False, verbosity=0)¶
Write this report to the disk as a collection of HTML documents.
 Parameters
path (str or pathlike object) – The filesystem path of a directory to write the report to. If the specified directory does not exist, it will be created automatically
auto_open (bool, optional) – Whether the output file should be automatically opened in a web browser.
link_to (list, optional) – If not None, a list of one or more items from the set {“tex”, “pdf”, “pkl”} indicating whether or not to create and include links to Latex, PDF, and Python pickle files, respectively.
connected (bool, optional) – Whether output HTML should assume an active internet connection. If True, then the resulting HTML file size will be reduced because it will link to web resources (e.g. CDN libraries) instead of embedding them.
build_options (dict) – Dict of options for building plots. Expected values are defined during construction of this report object.
brevity (int, optional) –
Amount of detail to include in the report. Larger values mean smaller “more briefr” reports, which reduce generation time, load time, and disk space consumption. In particular:
1: Plots showing persequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germlevel estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
precision (int or dict, optional) – The amount of precision to display. A dictionary with keys “polar”, “sci”, and “normal” can separately specify the precision for complex angles, numbers in scientific notation, and everything else, respectively. If an integer is given, it this same value is taken for all precision types. If None, then {‘normal’: 6, ‘polar’: 3, ‘sci’: 0} is used.
resizable (bool, optional) – Whether plots and tables are made with resize handles and can be resized within the report.
autosize ({'none', 'initial', 'continual'}) – Whether tables and plots should be resized, either initially – i.e. just upon first rendering (“initial”) – or whenever the browser window is resized (“continual”).
single_file (bool, optional) – If true, the report will be written to a single HTML document, with external dependencies bakedin. This mode is not recommended for large reports, because this file can grow large enough that major web browsers may struggle to render it.
verbosity (int, optional) – Amount of detail to print to stdout.
 write_notebook(self, path, auto_open=False, connected=False, verbosity=0)¶
Write this report to the disk as an IPython notebook
A notebook report allows the user to interact more flexibly with the data underlying the figures, and to easily generate customized variants on the figures. As such, this type of report will be most useful for experts who want to tinker with the standard analysis presented in the static HTML or LaTeX format reports.
 Parameters
path (str or pathlike object) – The filesystem path to write the report to. By convention, this should use the .ipynb file extension.
auto_open (bool, optional) – If True, automatically open the report in a web browser after it has been generated.
connected (bool, optional) – Whether output notebook should assume an active internet connection. If True, then the resulting file size will be reduced because it will link to web resources (e.g. CDN libraries) instead of embedding them.
verbosity (int, optional) – How much detail to send to stdout.
 write_pdf(self, path, latex_cmd='pdflatex', latex_flags=None, build_options=None, brevity=0, precision=None, auto_open=False, comm=None, verbosity=0)¶
Write this report to the disk as a PDF document.
 Parameters
path (str or pathlike object) – The filesystem path to write the report to. By convention, this should use the .pdf file extension.
latex_cmd (str, optional) – Shell command to run to compile a PDF document from the generated LaTeX source.
latex_flags ([str], optional) – List of flags to pass when calling latex_cmd.
build_options (dict) – Dict of options for building plots. Expected values are defined during construction of this report object.
brevity (int, optional) –
Amount of detail to include in the report. Larger values mean smaller “more briefr” reports, which reduce generation time, load time, and disk space consumption. In particular:
1: Plots showing persequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germlevel estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
precision (int or dict, optional) – The amount of precision to display. A dictionary with keys “polar”, “sci”, and “normal” can separately specify the precision for complex angles, numbers in scientific notation, and everything else, respectively. If an integer is given, it this same value is taken for all precision types. If None, then {‘normal’: 6, ‘polar’: 3, ‘sci’: 0} is used.
auto_open (bool, optional) – Whether the output file should be automatically opened in a web browser.
comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors.
verbosity (int, optional) – Amount of detail to print to stdout.
 class pygsti.report._ReportableQty(value, errbar=None, non_markovian_ebs=False)¶
Bases:
object
A computed quantity and possibly its error bars, primarily for use in reports.
 Parameters
value (object) – The value, usually a float or numpy array.
errbar (object, optional) – The (symmetric) error bar on value. If value is an array, errbar has the same shape. None is used to signify “no error bars”.
non_markovian_ebs (bool, optional) – Whether these error bars are “nonmarkovian”type error bars (it can be useful to keep track of this for formatting).
 size¶
Returns the size of this ReportableQty’s value.
 Type
int
 __str__(self)¶
Return str(self).
 __repr__(self)¶
Return repr(self).
 __add__(self, x)¶
 __mul__(self, x)¶
 __truediv__(self, x)¶
 __getstate__(self)¶
 __setstate__(self, d)¶
 __copy__(self)¶
 __deepcopy__(self, memo)¶
 log(self)¶
Returns a ReportableQty that is the logarithm of this one.
 Returns
ReportableQty
 real(self)¶
Returns a ReportableQty that is the real part of this one.
 Returns
ReportableQty
 imag(self)¶
Returns a ReportableQty that is the imaginary part of this one.
 Returns
ReportableQty
 absdiff(self, constant_value, separate_re_im=False)¶
Create a ReportableQty that is the difference between constant_value and this one.
The returned quantity’s value is given by (elementwise in the vector case):
abs(self  constant_value).
 Parameters
constant_value (float or numpy.ndarray) – The constant value to use.
separate_re_im (bool, optional) – When True, two separate real and imaginarypart
ReportableQty
objects are returned (applicable to complexvalued quantities).
 Returns
ReportableQty or tuple – The output ReportableQty`(s). If `separate_re_im=True then a 2tuple of (realpart, imaginarypart) quantities is returned. Otherwise a single quantity is returned.
 infidelity_diff(self, constant_value)¶
Creates a ReportableQty that is the difference between constant_value and this one.
The returned quantity’s value is given by (elementwise in the vector case):
1.0  Re(conjugate(constant_value) * self )
 Parameters
constant_value (float or numpy.ndarray) – The constant value to use.
 Returns
ReportableQty
 mod(self, x)¶
Creates a ReportableQty that holds this_qty mod x.
That is, the value and error bar (if present) are modulusdivided by x.
 Parameters
x (int) – Value to modulusdivide by.
 Returns
ReportableQty
 hermitian_to_real(self)¶
Creates a ReportableQty that holds a real “version” of a Hermitian matrix.
Specifically, the returned quantity’s value is the real matrix whose upper/lower triangle contains the real/imaginary parts of the corresponding offdiagonal matrix elements of the Hermitian matrix stored in this ReportableQty.
This is used for display purposes. If this object doesn’t contain a Hermitian matrix, ValueError is raised.
 Returns
ReportableQty
 reshape(self, *args)¶
Returns a ReportableQty whose underlying values are reshaped.
 Returns
ReportableQty
 property size(self)¶
Returns the size of this ReportableQty’s value.
 Returns
int
 static from_val(value, non_markovian_ebs=False)¶
Convert Table values into ReportableQtys or leave them be if they are wellformed types.
Wellformed types include:  strings  figures  :class:`ReportableQty`s
A tuple will be converted to a
ReportableQty
holding the first field as a value and second field as an error bar. Anything else will be converted to a ReportableQty with no error bars. Parameters
value (object) – The value to convert.
non_markovian_ebs (bool, optional) – Whether the error bars are of the “nonmarkovian”type.
 Returns
ReportableQty
 property has_errorbar(self)¶
Return whether this quantity is storing an error bar (bool).
 Returns
bool
 scale_inplace(self, factor)¶
Scale the value and error bar (if present) by factor.
 Parameters
factor (float) – The scaling factor.
 Returns
None
 property value(self)¶
Returns the quantity’s value
 Returns
object – Usually a float or numpy array.
 property errorbar(self)¶
Returns the quantity’s error bar(s)
 Returns
object – Usually a float or numpy array.
 property value_and_errorbar(self)¶
Returns the quantity’s value and error bar(s)
 Returns
value (object) – This object’s value (usually a float or numpy array).
error_bar (object) – This object’s value (usually a float or numpy array).
 render_with(self, f, specs=None, ebstring='%s +/ %s', nmebstring=None)¶
Render this ReportableQty using the function f.
 Parameters
f (function) – The formatter function which separately converts the stored value and error bar (if present) to string quantities that are then formatted using ebstring, nmebstring or just “%s” (if there’s no error bar). This function must have the signature f(val, specs) where val is either the value or error bar and specs is a dictionary given by the next argument.
specs (dict, optional) – Additional parameters to pass to the formatter function f.
ebstring (str, optional) – format string that describes how to display the value and error bar after they are rendered as string (ebstring should have two `%s`s in it).
nmebstring (str, optional) – format string, similar to ebstring, for displaying nonMarkovian error bars (if None then ebstring is used).
 Returns
str
 class pygsti.report._Basis(name, longname, real, sparse)¶
Bases:
pygsti.baseobjs.nicelyserializable.NicelySerializable
An ordered set of labeled matrices/vectors.
The base class for basis objects. A basis in pyGSTi is an abstract notion of a set of labeled elements, or “vectors”. Each basis has a certain size, and has .elements, .labels, and .ellookup members, the latter being a dictionary mapping of labels to elements.
An important point to note that isn’t immediately intuitive is that while Basis object holds elements (in its .elements property) these are not the same as its vectors (given by the object’s vector_elements property). Often times, in what we term a “simple” basis, the you just flatten an element to get the corresponding vectorelement. This works for bases where the elements are either vectors (where flattening does nothing) and matrices. By storing elements as distinct from vector_elements, the Basis can capture additional structure of the elements (such as viewing them as matrices) that can be helpful for their display and interpretation. The elements are also sometimes referred to as the “natural elements” because they represent how to display the element in a natrual way. A nonsimple basis occurs when vector_elements need to be stored as elements in a larger “embedded” way so that these elements can be displayed and interpeted naturally.
A second important note is that there is assumed to be some underlying “standard” basis underneath all the bases in pyGSTi. The elements in a Basis are always written in this standard basis. In the case of the “std”named basis in pyGSTi, these elements are just the trivial vector or matrix units, so one can rightly view the “std” pyGSTi basis as the “standard” basis for a that particular dimension.
The arguments below describe the basic properties of all basis objects in pyGSTi. It is important to remember that the vector_elements of a basis are different from its elements (see the
Basis
docstring), and that dim refers to the vector elements whereas elshape refers to the elements.For example, consider a 2element Basis containing the I and X Pauli matrices. The size of this basis is 2, as there are two elements (and two vector elements). Since vector elements are the length4 flattened Pauli matrices, the dimension (dim) is 4. Since the elements are 2x2 Pauli matrices, the elshape is (2,2).
As another example consider a basis which spans all the diagonal 2x2 matrices. The elements of this basis are the two matrix units with a 1 in the (0,0) or (1,1) location. The vector elements, however, are the length2 [1,0] and [0,1] vectors obtained by extracting just the diagonal entries from each basis element. Thus, for this basis, size=2, dim=2, and elshape=(2,2)  so the dimension is not just the product of elshape entries (equivalently, elsize).
 Parameters
name (string) – The name of the basis. This can be anything, but is usually short and abbreviated. There are several types of bases built into pyGSTi that can be constructed by this name.
longname (string) – A more descriptive name for the basis.
real (bool) – Elements and vector elements are always allowed to have complex entries. This argument indicates whether the coefficients in the expression of an arbitrary vector in this basis must be real. For example, if real=True, then when pyGSTi transforms a vector in some other basis to a vector in this basis, it will demand that the values of that vector (i.e. the coefficients which multiply this basis’s elements to obtain a vector in the “standard” basis) are real.
sparse (bool) – Whether the elements of .elements for this Basis are stored (when they are stored at all) as sparse matrices or vectors.
 dim¶
The dimension of the vector space this basis fully or partially spans. Equivalently, the length of the vector_elements of the basis.
 Type
int
 size¶
The number of elements (or vectorelements) in the basis.
 Type
int
 elshape¶
The shape of each element. Typically either a length1 or length2 tuple, corresponding to vector or matrix elements, respectively. Note that vector elements always have shape (dim,) (or (dim,1) in the sparse case).
 Type
int
 elndim¶
The number of element dimensions, i.e. len(self.elshape)
 Type
int
 elsize¶
The total element size, i.e. product(self.elshape)
 Type
int
 vector_elements¶
The “vectors” of this basis, always 1D (sparse or dense) arrays.
 Type
list
 classmethod cast(cls, name_or_basis_or_matrices, dim=None, sparse=None, classical_name='cl')¶
Convert various things that can describe a basis into a Basis object.
 Parameters
name_or_basis_or_matrices (various) –
Can take on a variety of values to produce different types of bases:
None: an empty ExpicitBasis
Basis: checked with dim and sparse and passed through.
str: BuiltinBasis or DirectSumBasis with the given name.
 list: an ExplicitBasis if given matrices/vectors or a
DirectSumBasis if given a (name, dim) pairs.
dim (int or StateSpace, optional) – The dimension of the basis to create. Sometimes this can be inferred based on name_or_basis_or_matrices, other times it must be supplied. This is the dimension of the space that this basis fully or partially spans. This is equal to the number of basis elements in a “full” (ordinary) basis. When a StateSpace object is given, a more detailed directsumoftensorproductblocks structure for the state space (rather than a single dimension) is described, and a basis is produced for this space. For instance, a DirectSumBasis basis of TensorProdBasis components can result when there are multiple tensorproduct blocks and these blocks consist of multiple factors.
sparse (bool, optional) – Whether the resulting basis should be “sparse”, meaning that its elements will be sparse rather than dense matrices.
classical_name (str, optional) – An alternate builtin basis name that should be used when constructing the bases for the classical sectors of dim, when dim is a StateSpace object.
 Returns
Basis
 property dim(self)¶
The dimension of the vector space this basis fully or partially spans. Equivalently, the length of the vector_elements of the basis.
 property size(self)¶
The number of elements (or vectorelements) in the basis.
 property elshape(self)¶
The shape of each element. Typically either a length1 or length2 tuple, corresponding to vector or matrix elements, respectively. Note that vector elements always have shape (dim,) (or (dim,1) in the sparse case).
 property elndim(self)¶
The number of element dimensions, i.e. len(self.elshape)
 Returns
int
 property elsize(self)¶
The total element size, i.e. product(self.elshape)
 Returns
int
 is_simple(self)¶
Whether the flattenedelement vector space is the same space as the space this basis’s vectors belong to.
 Returns
bool
 is_complete(self)¶
Whether this is a complete basis, i.e. this basis’s vectors span the entire space that they live in.
 Returns
bool
 is_partial(self)¶
The negative of :method:`is_complete`, effectively “is_incomplete”.
 Returns
bool
 property vector_elements(self)¶
The “vectors” of this basis, always 1D (sparse or dense) arrays.
 Returns
list – A list of 1D arrays.
 copy(self)¶
Make a copy of this Basis object.
 Returns
Basis
 with_sparsity(self, desired_sparsity)¶
Returns either this basis or a copy of it with the desired sparsity.
If this basis has the desired sparsity it is simply returned. If not, this basis is copied to one that does.
 Parameters
desired_sparsity (bool) – The sparsity (True for sparse elements, False for dense elements) that is desired.
 Returns
Basis
 abstract _copy_with_toggled_sparsity(self)¶
 __str__(self)¶
Return str(self).
 __getitem__(self, index)¶
 __len__(self)¶
 __eq__(self, other)¶
Return self==value.
 create_transform_matrix(self, to_basis)¶
Get the matrix that transforms a vector from this basis to to_basis.
 Parameters
to_basis (Basis or string) – The basis to transform to or a builtin basis name. In the latter case, a basis to transform to is built with the same structure as this basis but with all components constructed from the given name.
 Returns
numpy.ndarray (even if basis is sparse)
 reverse_transform_matrix(self, from_basis)¶
Get the matrix that transforms a vector from from_basis to this basis.
The reverse of :method:`create_transform_matrix`.
 Parameters
from_basis (Basis or string) – The basis to transform from or a builtin basis name. In the latter case, a basis to transform from is built with the same structure as this basis but with all components constructed from the given name.
 Returns
numpy.ndarray (even if basis is sparse)
 is_normalized(self)¶
Check if a basis is normalized, meaning that Tr(Bi Bi) = 1.0.
Available only to bases whose elements are matrices for now.
 Returns
bool
 property to_std_transform_matrix(self)¶
Retrieve the matrix that transforms a vector from this basis to the standard basis of this basis’s dimension.
 Returns
numpy array or scipy.sparse.lil_matrix – An array of shape (dim, size) where dim is the dimension of this basis (the length of its vectors) and size is the size of this basis (its number of vectors).
 property from_std_transform_matrix(self)¶
Retrieve the matrix that transforms vectors from the standard basis to this basis.
 Returns
numpy array or scipy sparse matrix – An array of shape (size, dim) where dim is the dimension of this basis (the length of its vectors) and size is the size of this basis (its number of vectors).
 property to_elementstd_transform_matrix(self)¶
Get transformation matrix from this basis to the “element space”.
Get the matrix that transforms vectors in this basis (with length equal to the dim of this basis) to vectors in the “element space”  that is, vectors in the same standard basis that the elements of this basis are expressed in.
 Returns
numpy array – An array of shape (element_dim, size) where element_dim is the dimension, i.e. size, of the elements of this basis (e.g. 16 if the elements are 4x4 matrices) and size is the size of this basis (its number of vectors).
 property from_elementstd_transform_matrix(self)¶
Get transformation matrix from “element space” to this basis.
Get the matrix that transforms vectors in the “element space”  that is, vectors in the same standard basis that the elements of this basis are expressed in  to vectors in this basis (with length equal to the dim of this basis).
 Returns
numpy array – An array of shape (size, element_dim) where element_dim is the dimension, i.e. size, of the elements of this basis (e.g. 16 if the elements are 4x4 matrices) and size is the size of this basis (its number of vectors).
 create_equivalent(self, builtin_basis_name)¶
Create an equivalent basis with components of type builtin_basis_name.
Create a
Basis
that is equivalent in structure & dimension to this basis but whose simple components (perhaps just this basis itself) is of the builtin basis type given by builtin_basis_name. Parameters
builtin_basis_name (str) – The name of a builtin basis, e.g. “pp”, “gm”, or “std”. Used to construct the simple components of the returned basis.
 Returns
Basis
 create_simple_equivalent(self, builtin_basis_name=None)¶
Create a basis of type builtin_basis_name whose elements are compatible with this basis.
Create a simple basis and one without components (e.g. a
TensorProdBasis
, is a simple basis w/components) of the builtin type specified whose dimension is compatible with the elements of this basis. This function might also be named “element_equivalent”, as it returns the builtin_basis_nameanalogue of the standard basis that this basis’s elements are expressed in. Parameters
builtin_basis_name (str, optional) – The name of the builtin basis to use. If None, then a copy of this basis is returned (if it’s simple) or this basis’s name is used to try to construct a simple and componentfree version of the same builtinbasis type.
 Returns
Basis
 is_compatible_with_state_space(self, state_space)¶
Checks whether this basis is compatible with a given state space.
 Parameters
state_space (StateSpace) – the state space to check.
 Returns
bool
 class pygsti.report._DirectSumBasis(component_bases, name=None, longname=None)¶
Bases:
LazyBasis
A basis that is the direct sum of one or more “component” bases.
Elements of this basis are the union of the basis elements on each component, each embedded into a common blockdiagonal structure where each component occupies its own block. Thus, when there is more than one component, a DirectSumBasis is not a simple basis because the size of its elements is larger than the size of its vector space (which corresponds to just the diagonal blocks of its elements).
 Parameters
component_bases (iterable) – A list of the component bases. Each list elements may be either a Basis object or a tuple of arguments to :function:`Basis.cast`, e.g. (‘pp’,4).
name (str, optional) – The name of this basis. If None, the names of the component bases joined with “+” is used.
longname (str, optional) – A longer description of this basis. If None, then a long name is automatically generated.
 vector_elements¶
The “vectors” of this basis, always 1D (sparse or dense) arrays.
 Type
list
 _to_nice_serialization(self)¶
 classmethod _from_nice_serialization(cls, state)¶
 property dim(self)¶
The dimension of the vector space this basis fully or partially spans. Equivalently, the length of the vector_elements of the basis.
 property size(self)¶
The number of elements (or vectorelements) in the basis.
 property elshape(self)¶
The shape of each element. Typically either a length1 or length2 tuple, corresponding to vector or matrix elements, respectively. Note that vector elements always have shape (dim,) (or (dim,1) in the sparse case).
 __hash__(self)¶
Return hash(self).
 _lazy_build_vector_elements(self)¶
 _lazy_build_elements(self)¶
 _lazy_build_labels(self)¶
 _copy_with_toggled_sparsity(self)¶
 __eq__(self, other)¶
Return self==value.
 property vector_elements(self)¶
The “vectors” of this basis, always 1D (sparse or dense) arrays.
 Returns
list
 property to_std_transform_matrix(self)¶
Retrieve the matrix that transforms a vector from this basis to the standard basis of this basis’s dimension.
 Returns
numpy array or scipy.sparse.lil_matrix – An array of shape (dim, size) where dim is the dimension of this basis (the length of its vectors) and size is the size of this basis (its number of vectors).
 property to_elementstd_transform_matrix(self)¶
Get transformation matrix from this basis to the “element space”.
Get the matrix that transforms vectors in this basis (with length equal to the dim of this basis) to vectors in the “element space”  that is, vectors in the same standard basis that the elements of this basis are expressed in.
 Returns
numpy array – An array of shape (element_dim, size) where element_dim is the dimension, i.e. size, of the elements of this basis (e.g. 16 if the elements are 4x4 matrices) and size is the size of this basis (its number of vectors).
 create_equivalent(self, builtin_basis_name)¶
Create an equivalent basis with components of type builtin_basis_name.
Create a Basis that is equivalent in structure & dimension to this basis but whose simple components (perhaps just this basis itself) is of the builtin basis type given by builtin_basis_name.
 Parameters
builtin_basis_name (str) – The name of a builtin basis, e.g. “pp”, “gm”, or “std”. Used to construct the simple components of the returned basis.
 Returns
DirectSumBasis
 create_simple_equivalent(self, builtin_basis_name=None)¶
Create a basis of type builtin_basis_name whose elements are compatible with this basis.
Create a simple basis and one without components (e.g. a
TensorProdBasis
, is a simple basis w/components) of the builtin type specified whose dimension is compatible with the elements of this basis. This function might also be named “element_equivalent”, as it returns the builtin_basis_nameanalogue of the standard basis that this basis’s elements are expressed in. Parameters
builtin_basis_name (str, optional) – The name of the builtin basis to use. If None, then a copy of this basis is returned (if it’s simple) or this basis’s name is used to try to construct a simple and componentfree version of the same builtinbasis type.
 Returns
Basis
 class pygsti.report._Lbl¶
Bases:
object
A label used to identify a gate, circuit layer, or (sub)circuit.
A label consisting of a string along with a tuple of integers or sectornames specifying which qubits, or more generally, parts of the Hilbert space that is acted upon by an object solabeled.
 property depth(self)¶
The depth of this label, viewed as a subcircuit.
 property reps(self)¶
Number of repetitions (of this label’s components) that this label represents.
 property has_nontrivial_components(self)¶
 collect_args(self)¶
 strip_args(self)¶
 expand_subcircuits(self)¶
Expand any subcircuits within this label.
Returns a list of component labels which doesn’t include any
CircuitLabel
labels. This effectively expands any “boxes” or “exponentiation” within this label. Returns
tuple – A tuple of component Labels (none of which should be :class:`CircuitLabel`s).
 pygsti.report._CVXPY_AVAILABLE¶
 pygsti.report.FINITE_DIFF_EPS = 1e07¶
 pygsti.report._null_fn(*arg)¶
 pygsti.report._project_to_valid_prob(p, tol=1e09)¶
 pygsti.report._make_reportable_qty_or_dict(f0, df=None, non_markovian_ebs=False)¶
Just adds special processing with f0 is a dict, where we return a dict or ReportableQtys rather than a single ReportableQty of the dict.
 pygsti.report.evaluate(model_fn, cri=None, verbosity=0)¶
Evaluate a ModelFunction object using confidence region information
 Parameters
model_fn (ModelFunction) – The function to evaluate
cri (ConfidenceRegionFactoryView, optional) – View for computing confidence intervals.
verbosity (int, optional) – Amount of detail to print to stdout.
 Returns
ReportableQty or dict – If model_fn does returns a dict of ReportableQty objects, otherwise a single ReportableQty.
 pygsti.report.spam_dotprods(rho_vecs, povms)¶
SPAM dot products (concatenates POVMS)
 Parameters
rho_vecs (list) – A list of
State
objects.povms (list) – A list of
POVM
objects.
 Returns
numpy.ndarray – A 2D array of shape (len(rho_vecs), num_evecs) where num_evecs is the total number of effect vectors in all of povms.
 pygsti.report.Spam_dotprods¶
 pygsti.report.choi_matrix(gate, mx_basis)¶
Choi matrix
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that gate is in.
 Returns
numpy.ndarray
 pygsti.report.Choi_matrix¶
 pygsti.report.choi_eigenvalues(gate, mx_basis)¶
Choi matrix eigenvalues
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that gate is in.
 Returns
numpy.ndarray
 pygsti.report.Choi_evals¶
 pygsti.report.choi_trace(gate, mx_basis)¶
Trace of the Choi matrix
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that gate is in.
 Returns
float
 pygsti.report.Choi_trace¶
 class pygsti.report.GateEigenvalues(model, oplabel)¶
Bases:
pygsti.report.modelfunction.ModelFunction
Gate eigenvalues
 Parameters
 class pygsti.report.CircuitEigenvalues(model, circuit)¶
Bases:
pygsti.report.modelfunction.ModelFunction
Circuit eigenvalues
 Parameters
 pygsti.report.rel_circuit_eigenvalues(model_a, model_b, circuit)¶
Eigenvalues of dot(productB(circuit)^1, productA(circuit))
 pygsti.report.Rel_circuit_eigenvalues¶
 pygsti.report.circuit_frobenius_diff(model_a, model_b, circuit)¶
Frobenius distance btwn productA(circuit) and productB(circuit)
 pygsti.report.Circuit_fro_diff¶
 pygsti.report.circuit_entanglement_infidelity(model_a, model_b, circuit)¶
Entanglement infidelity btwn productA(circuit) and productB(circuit)
 pygsti.report.Circuit_entanglement_infidelity¶
 pygsti.report.circuit_avg_gate_infidelity(model_a, model_b, circuit)¶
Average gate infidelity between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_avg_gate_infidelity¶
 pygsti.report.circuit_jtrace_diff(model_a, model_b, circuit)¶
Jamiolkowski trace distance between productA(circuit) and productB(circuit)
 pygsti.report.Circuit_jt_diff¶
 class pygsti.report.CircuitHalfDiamondNorm(model_a, model_b, circuit)¶
Bases:
pygsti.report.modelfunction.ModelFunction
1/2 diamond norm of difference between productA(circuit) and productB(circuit)
 Parameters
 pygsti.report.circuit_nonunitary_entanglement_infidelity(model_a, model_b, circuit)¶
Nonunitary entanglement infidelity between productA(circuit) and productB(circuit)
 pygsti.report.Circuit_nonunitary_entanglement_infidelity¶
 pygsti.report.circuit_nonunitary_avg_gate_infidelity(model_a, model_b, circuit)¶
Nonunitary average gate infidelity between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_nonunitary_avg_gate_infidelity¶
 pygsti.report.circuit_eigenvalue_entanglement_infidelity(model_a, model_b, circuit)¶
Eigenvalue entanglement infidelity between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_eigenvalue_entanglement_infidelity¶
 pygsti.report.circuit_eigenvalue_avg_gate_infidelity(model_a, model_b, circuit)¶
Eigenvalue average gate infidelity between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_eigenvalue_avg_gate_infidelity¶
 pygsti.report.circuit_eigenvalue_nonunitary_entanglement_infidelity(model_a, model_b, circuit)¶
Eigenvalue nonunitary entanglement infidelity between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_eigenvalue_nonunitary_entanglement_infidelity¶
 pygsti.report.circuit_eigenvalue_nonunitary_avg_gate_infidelity(model_a, model_b, circuit)¶
Eigenvalue nonunitary average gate infidelity between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_eigenvalue_nonunitary_avg_gate_infidelity¶
 pygsti.report.circuit_eigenvalue_diamondnorm(model_a, model_b, circuit)¶
Eigenvalue diamond distance between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_eigenvalue_diamondnorm¶
 pygsti.report.circuit_eigenvalue_nonunitary_diamondnorm(model_a, model_b, circuit)¶
Eigenvalue nonunitary diamond distance between productA(circuit) and productB(circuit).
 pygsti.report.Circuit_eigenvalue_nonunitary_diamondnorm¶
 pygsti.report.povm_entanglement_infidelity(model_a, model_b, povmlbl)¶
POVM entanglement infidelity between model_a and model_b.
Equal to 1  entanglement_fidelity(POVM_MAP) where POVM_MAP is the extension of the POVM from the classical space of koutcomes to the space of (diagonal) k by k density matrices.
 pygsti.report.POVM_entanglement_infidelity¶
 pygsti.report.povm_jtrace_diff(model_a, model_b, povmlbl)¶
POVM Jamiolkowski trace distance between model_a and model_b
Equal to Jamiolkowski_trace_distance(POVM_MAP) where POVM_MAP is the extension of the POVM from the classical space of koutcomes to the space of (diagonal) k by k density matrices.
 pygsti.report.POVM_jt_diff¶
 pygsti.report.povm_half_diamond_norm(model_a, model_b, povmlbl)¶
Half the POVM diamond distance between model_a and model_b.
Equal to half_diamond_dist(POVM_MAP) where POVM_MAP is the extension of the POVM from the classical space of koutcomes to the space of (diagonal) k by k density matrices.
 pygsti.report.decomposition(gate)¶
DEPRECATED: Decompose a 1Q gate into rotations about axes.
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
 Returns
ReportableQty
 pygsti.report.upper_bound_fidelity(gate, mx_basis)¶
Upper bound on entanglement fidelity
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that gate is in.
 Returns
float
 pygsti.report.Upper_bound_fidelity¶
 pygsti.report.closest_ujmx(gate, mx_basis)¶
Jamiolkowski state of closest unitary to gate
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that gate is in.
 Returns
float
 pygsti.report.Closest_ujmx¶
 pygsti.report.maximum_fidelity(gate, mx_basis)¶
Fidelity between gate and its closest unitary
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that gate is in.
 Returns
float
 pygsti.report.Maximum_fidelity¶
 pygsti.report.maximum_trace_dist(gate, mx_basis)¶
Jamiolkowski trace distance between gate and its closest unitary
 Parameters
gate (numpy.ndarray) – the transfermatrix specifying a gate’s action.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that gate is in.
 Returns
float
 pygsti.report.Maximum_trace_dist¶
 pygsti.report.angles_btwn_rotn_axes(model)¶
Array of angles between the rotation axes of the gates of model.
 Parameters
model (Model) – The model to process.
 Returns
numpy.ndarray – Of size (nOperations,nGate) where nOperations=len(model.operations)
 pygsti.report.Angles_btwn_rotn_axes¶
 pygsti.report.entanglement_fidelity(a, b, mx_basis)¶
Entanglement fidelity between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Entanglement_fidelity¶
 pygsti.report.entanglement_infidelity(a, b, mx_basis)¶
Entanglement infidelity between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Entanglement_infidelity¶
 pygsti.report.closest_unitary_fidelity(a, b, mx_basis)¶
Entanglement infidelity between closest unitaries to a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Closest_unitary_fidelity¶
 pygsti.report.frobenius_diff(a, b, mx_basis)¶
Frobenius distance between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Fro_diff¶
 pygsti.report.jtrace_diff(a, b, mx_basis)¶
Jamiolkowski trace distance between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Jt_diff¶
 class pygsti.report.HalfDiamondNorm(model_a, model_b, oplabel)¶
Bases:
pygsti.report.modelfunction.ModelFunction
Half the diamond distance bewteen model_a.operations[op_label] and model_b.operations[op_label]
 Parameters
 pygsti.report.std_unitarity(a, b, mx_basis)¶
a gaugeinvariant quantity that behaves like the unitarity
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.eigenvalue_unitarity(a, b)¶
a gaugeinvariant quantity that behaves like the unitarity
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
 Returns
float
 pygsti.report.nonunitary_entanglement_infidelity(a, b, mx_basis)¶
Returns (d^2  1)/d^2 * (1  sqrt(U)), where U is the unitarity of a*b^{1}
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Nonunitary_entanglement_infidelity¶
 pygsti.report.nonunitary_avg_gate_infidelity(a, b, mx_basis)¶
Returns (d  1)/d * (1  sqrt(U)), where U is the unitarity of a*b^{1}
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Nonunitary_avg_gate_infidelity¶
 pygsti.report.eigenvalue_nonunitary_entanglement_infidelity(a, b, mx_basis)¶
Returns (d^2  1)/d^2 * (1  sqrt(U)), where U is the eigenvalueunitarity of a*b^{1}
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Eigenvalue_nonunitary_entanglement_infidelity¶
 pygsti.report.eigenvalue_nonunitary_avg_gate_infidelity(a, b, mx_basis)¶
Returns (d  1)/d * (1  sqrt(U)), where U is the eigenvalueunitarity of a*b^{1}
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Eigenvalue_nonunitary_avg_gate_infidelity¶
 pygsti.report.eigenvalue_entanglement_infidelity(a, b, mx_basis)¶
Eigenvalue entanglement infidelity between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Eigenvalue_entanglement_infidelity¶
 pygsti.report.eigenvalue_avg_gate_infidelity(a, b, mx_basis)¶
Eigenvalue average gate infidelity between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Eigenvalue_avg_gate_infidelity¶
 pygsti.report.eigenvalue_diamondnorm(a, b, mx_basis)¶
Eigenvalue diamond distance between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Eigenvalue_diamondnorm¶
 pygsti.report.eigenvalue_nonunitary_diamondnorm(a, b, mx_basis)¶
Eigenvalue nonunitary diamond distance between a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Eigenvalue_nonunitary_diamondnorm¶
 pygsti.report.avg_gate_infidelity(a, b, mx_basis)¶
Returns the average gate infidelity between a and b, where b is the “target” operation.
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Avg_gate_infidelity¶
 pygsti.report.model_model_angles_btwn_axes(a, b, mx_basis)¶
Angle between the rotation axes of a and b (1qubit gates)
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Model_model_angles_btwn_axes¶
 pygsti.report.rel_eigenvalues(a, b, mx_basis)¶
Eigenvalues of b^{1} * a
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
numpy.ndarray
 pygsti.report.Rel_eigvals¶
 pygsti.report.rel_log_tig_eigenvalues(a, b, mx_basis)¶
Eigenvalues of log(b^{1} * a)
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
numpy.ndarray
 pygsti.report.Rel_logTiG_eigvals¶
 pygsti.report.rel_log_gti_eigenvalues(a, b, mx_basis)¶
Eigenvalues of log(a * b^{1})
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
numpy.ndarray
 pygsti.report.Rel_logGTi_eigvals¶
 pygsti.report.rel_log_diff_eigenvalues(a, b, mx_basis)¶
Eigenvalues of log(a)  log(b)
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
numpy.ndarray
 pygsti.report.Rel_logGmlogT_eigvals¶
 pygsti.report.rel_gate_eigenvalues(a, b, mx_basis)¶
Eigenvalues of b^{1} * a
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
numpy.ndarray
 pygsti.report.Rel_gate_eigenvalues¶
 pygsti.report.errorgen_and_projections(errgen, mx_basis)¶
Project errgen on all of the standard sets of error generators.
 Parameters
errgen (numpy.ndarray) – The error generator.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that errgen is in.
 Returns
dict – Dictionary of ‘error generator’, ‘X projections’, and ‘X projection power’ keys, where X is ‘hamiltonian’, ‘stochastic’, and ‘affine’.
 pygsti.report.log_tig_and_projections(a, b, mx_basis)¶
Projections of log(b^{1}*a).
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
dict – A dictionary of quantities with keys ‘error generator’, ‘X projections’, and ‘X projection power’, where X is ‘hamiltonian’, ‘stochastic’, and ‘affine’.
 pygsti.report.LogTiG_and_projections¶
 pygsti.report.log_gti_and_projections(a, b, mx_basis)¶
Projections of log(a*b^{1}).
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
dict – A dictionary of quantities with keys ‘error generator’, ‘X projections’, and ‘X projection power’, where X is ‘hamiltonian’, ‘stochastic’, and ‘affine’.
 pygsti.report.LogGTi_and_projections¶
 pygsti.report.log_diff_and_projections(a, b, mx_basis)¶
Projections of log(a)log(b).
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
dict – A dictionary of quantities with keys ‘error generator’, ‘X projections’, and ‘X projection power’, where X is ‘hamiltonian’, ‘stochastic’, and ‘affine’.
 pygsti.report.LogGmlogT_and_projections¶
 pygsti.report.robust_log_gti_and_projections(model_a, model_b, synthetic_idle_circuits)¶
Projections of log(A*B^{1}) using a gaugerobust technique.
 Parameters
 Returns
dict – A dictionary of quantities with keys ‘G error generator’, ‘G X projections’, and ‘G X projection power’, where G is a operation label and X is ‘hamiltonian’, ‘stochastic’, and ‘affine’.
 pygsti.report.Robust_LogGTi_and_projections¶
 pygsti.report.general_decomposition(model_a, model_b)¶
Decomposition of gates in model_a using those in model_b as their targets.
This function uses a generalized decomposition algorithm that can gates acting on a Hilbert space of any dimension.
 pygsti.report.General_decomposition¶
 pygsti.report.average_gateset_infidelity(model_a, model_b)¶
Average model infidelity
 pygsti.report.Average_gateset_infidelity¶
 pygsti.report.predicted_rb_number(model_a, model_b)¶
Prediction of RB number based on estimated (A) and target (B) models
 pygsti.report.Predicted_rb_number¶
 pygsti.report.vec_fidelity(a, b, mx_basis)¶
State fidelity between state vectors a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Vec_fidelity¶
 pygsti.report.vec_infidelity(a, b, mx_basis)¶
State infidelity fidelity between state vectors a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Vec_infidelity¶
 pygsti.report.vec_trace_diff(a, b, mx_basis)¶
Trace distance between state vectors a and b
 Parameters
a (numpy.ndarray) – The first process (transfer) matrix.
b (numpy.ndarray) – The second process (transfer) matrix.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Vec_tr_diff¶
 pygsti.report.vec_as_stdmx(vec, mx_basis)¶
State vector as a standard density matrix
 Parameters
vec (numpy.ndarray) – state vector as a 1D dense array.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that vec is in.
 Returns
numpy.ndarray – A 2D array (matrix) that is vec in the standard basis.
 pygsti.report.Vec_as_stdmx¶
 pygsti.report.vec_as_stdmx_eigenvalues(vec, mx_basis)¶
Eigenvalues of the density matrix corresponding to a state vector
 Parameters
vec (numpy.ndarray) – state vector as a 1D dense array.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that vec is in.
 Returns
numpy.ndarray
 pygsti.report.Vec_as_stdmx_eigenvalues¶
 pygsti.report.info_of_opfn_by_name(name)¶
Returns a nice humanreadable name and tooltip for a given gatefunction abbreviation.
 Parameters
name (str) –
An appreviation for a gatefunction name. Allowed values are:
”inf” : entanglement infidelity
”agi” : average gate infidelity
”trace” : 1/2 trace distance
”diamond” : 1/2 diamond norm distance
”nuinf” : nonunitary entanglement infidelity
”nuagi” : nonunitary entanglement infidelity
”evinf” : eigenvalue entanglement infidelity
”evagi” : eigenvalue average gate infidelity
”evnuinf” : eigenvalue nonunitary entanglement infidelity
”evnuagi” : eigenvalue nonunitary entanglement infidelity
”evdiamond” : eigenvalue 1/2 diamond norm distance
”evnudiamond” : eigenvalue nonunitary 1/2 diamond norm distance
”frob” : frobenius distance
”unmodeled” : unmodeled “wildcard” budget
 Returns
nicename (str)
tooltip (str)
 pygsti.report.evaluate_opfn_by_name(name, model, target_model, op_label_or_string, confidence_region_info)¶
Evaluates that gatefunction named by the abbreviation name.
 Parameters
name (str) – An appreviation for a operationfunction name. Allowed values are the same as those of
info_of_opfn_by_name()
.model (Model) – The model used by the operationfunction.
target_model (Model) – The target model.
op_label_or_string (str or Circuit or tuple) – The operation label or sequence of labels to compare. If a sequence of labels is given, then the “virtual gate” computed by taking the product of the specified gate matrices is compared.
confidence_region_info (ConfidenceRegion, optional) – If not None, specifies a confidenceregion used to compute error intervals.
 Returns
ReportableQty
 pygsti.report.instrument_infidelity(a, b, mx_basis)¶
Infidelity between instruments a and b
 Parameters
a (Instrument) – The first instrument.
b (Instrument) – The second instrument.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Instrument_infidelity¶
 pygsti.report.instrument_half_diamond_norm(a, b, mx_basis)¶
The diamond norm distance between instruments a and b.
 Parameters
a (Instrument) – The first instrument.
b (Instrument) – The second instrument.
mx_basis (Basis or {'pp', 'gm', 'std'}) – the basis that a and b are in.
 Returns
float
 pygsti.report.Instrument_half_diamond_norm¶
 pygsti.report.evaluate_instrumentfn_by_name(name, model, target_model, inst_label, confidence_region_info)¶
Evaluates that instrumentfunction named by the abbreviation name.
 Parameters
name (str) – An appreviation for a operationfunction name. Allowed values are the same as those of
info_of_opfn_by_name()
.model (Model) – The model used by the operationfunction.
target_model (Model) – The target model.
inst_label (Label) – The instrument label to compare.
confidence_region_info (ConfidenceRegion, optional) – If not None, specifies a confidenceregion used to compute error intervals.
 Returns
ReportableQty
 class pygsti.report.Workspace(cachefile=None)¶
Bases:
object
Central to data analysis, Workspace objects facilitate the building of reports and dashboards.
In particular, they serve as a:
factory for tables, plots, and other types of output
cache manager to optimize the construction of such output
serialization manager for saving and loading analysis variables
Workspace objects are typically used either 1) within an ipython notebook to interactively build a report/dashboard, or 2) within a script to build a hardcoded (“fixed”) report/dashboard.
 Parameters
cachefile (str, optional) – filename with cached workspace results
 save_cache(self, cachefile, show_unpickled=False)¶
Save this Workspace’s cache to a file.
 Parameters
cachefile (str) – The filename to save the cache to.
show_unpickled (bool, optional) – Whether to print quantities (keys) of cache that could not be saved because they were not pickleable.
 Returns
None
 load_cache(self, cachefile)¶
Load this Workspace’s cache from cachefile.
 Parameters
cachefile (str) – The filename to load the cache from.
 Returns
None
 __getstate__(self)¶
 __setstate__(self, state_dict)¶
 _makefactory(self, cls, autodisplay)¶
 _register_components(self, autodisplay)¶
 init_notebook_mode(self, connected=False, autodisplay=False)¶
Initialize this Workspace for use in an iPython notebook environment.
This function should be called prior to using the Workspace when working within an iPython notebook.
 Parameters
connected (bool , optional) – Whether to assume you are connected to the internet. If you are, then setting this to True allows initialization to rely on web hosted resources which will reduce the overall size of your notebook.
autodisplay (bool , optional) – Whether to automatically display workspace objects after they are created.
 Returns
None
 switched_compute(self, fn, *args)¶
Calls a compute function with special handling of
SwitchedValue
arguments.This is similar to calling fn, given its name and arguments, when some or all of those arguments are
SwitchedValue
objects.Caching is employed to avoid duplicating function evaluations which have the same arguments. Note that the function itself doesn’t need to deal with SwitchValue objects, as this routine resolves such objects into a series of function evaluations using the underlying value(s) within the SwitchValue. This routine is primarily used internally for the computation of tables and plots.
if any of the arguments is an instance of NotApplicable then fn is not evaluated and the instance is returned as the evaluation result. If multiple arguments are NotApplicable instances, the first is used as the result.
 Parameters
fn (function) – The function to evaluate
 Returns
fn_values (list) – The function return values for all relevant sets of arguments. Denote the length of this list by N.
switchboards (list) – A list of all the relevant Switchboards used during the function evaluation. Denote the length of this list by M.
switchboard_switch_indices (list) – A list of length M whose elements are tuples containing the 0based indices of the relevant switches (i.e. those used by any of the arguments) for each switchboard (element of switchboards).
switchpos_map (dict) – A dictionary whose keys are switch positions, and whose values are integers between 0 and N which index the element of fn_values corresponding to the given switch positions. Each “switch positions” key is a tuple of length M whose elements (one per switchboard) are tuples of 0based switchposition indices indicating the position of the relevant switches of that switchboard. Thus, len(key[i]) = len(switchboard_switch_indices[i]), where key is a dictionary key.
 pygsti.report._pygsti_version = 0.9.10.post64¶
 class pygsti.report._ExplicitOpModel(state_space, basis='pp', default_gate_type='full', default_prep_type='auto', default_povm_type='auto', default_instrument_type='auto', prep_prefix='rho', effect_prefix='E', gate_prefix='G', povm_prefix='M', instrument_prefix='I', simulator='auto', evotype='default')¶
Bases:
pygsti.models.model.OpModel
Encapsulates a set of gate, state preparation, and POVM effect operations.
An ExplictOpModel stores a set of labeled LinearOperator objects and provides dictionarylike access to their matrices. State preparation and POVM effect operations are represented as column vectors.
 Parameters
state_space (StateSpace) – The state space for this model.
basis ({"pp","gm","qt","std","sv"} or Basis, optional) – The basis used for the state space by dense superoperator representations.
default_param ({"full", "TP", "CPTP", etc.}, optional) – Specifies the default gate and SPAM vector parameterization type. Can be any value allowed by :method:`set_all_parameterizations`, which also gives a description of each parameterization type.
prep_prefix (string, optional) – Key prefixe for state preparations, allowing the model to determing what type of object a key corresponds to.
effect_prefix (string, optional) – Key prefix for POVM effects, allowing the model to determing what type of object a key corresponds to.
gate_prefix (string, optional) – Key prefix for gates, allowing the model to determing what type of object a key corresponds to.
povm_prefix (string, optional) – Key prefix for POVMs, allowing the model to determing what type of object a key corresponds to.
instrument_prefix (string, optional) – Key prefix for instruments, allowing the model to determing what type of object a key corresponds to.
simulator (ForwardSimulator or {"auto", "matrix", "map"}) –
The circuit simulator used to compute any requested probabilities, e.g. from :method:`probs` or :method:`bulk_probs`. The default value of “auto” automatically selects the simulation type, and is usually what you want. Other special allowed values are:
”matrix” : op_matrixop_matrix products are computed and cached to get composite gates which can then quickly simulate a circuit for any preparation and outcome. High memory demand; best for a small number of (1 or 2) qubits.
”map” : op_matrixstate_vector products are repeatedly computed to simulate circuits. Slower for a small number of qubits, but faster and more memory efficient for higher numbers of qubits (3+).
evotype (Evotype or str, optional) – The evolution type of this model, describing how states are represented. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
 _strict = False¶
 property _primitive_prep_label_dict(self)¶
 property _primitive_povm_label_dict(self)¶
 property _primitive_op_label_dict(self)¶
 property _primitive_instrument_label_dict(self)¶
 _iter_parameterized_objs(self)¶
 _excalc(self)¶
Create & return a special explicitmodel calculator for this model
 _embed_operation(self, op_target_labels, op_val, force=False)¶
Called by OrderedMemberDict._auto_embed to create an embeddedgate object that embeds op_val into the subspace of self.state_space given by op_target_labels.
 Parameters
op_target_labels (list) – A list of op_val’s target state space labels.
op_val (LinearOperator) – The gate object to embed. Note this should be a legitimate LinearOperatorderived object and not just a numpy array.
force (bool, optional) – Always wrap with an embedded LinearOperator, even if the dimension of op_val is the full model dimension.
 Returns
LinearOperator – A gate of the full model dimension.
 property default_gauge_group(self)¶
Gets the default gauge group for performing gauge transformations on this Model.
 Returns
GaugeGroup
 property prep(self)¶
The unique state preparation in this model, if one exists.
If not, a ValueError is raised.
 Returns
State
 property effects(self)¶
The effect vectors from the unique POVM in this model, if one exists.
If not, a ValueError is raised.
 Returns
list of POVMEffects
 __setitem__(self, label, value)¶
Set an operator, state, or POVM associated with a given label.
 Parameters
label (string) – the gate or SPAM vector label.
value (numpy array or LinearOperator or State or POVM) – a operation matrix, state vector, or POVM, which must have the appropriate state space for the Model and appropriate type given the prefix of the label.
 __getitem__(self, label)¶
Get an operation, state, or POVM associated with a given label.
 Parameters
label (string) – the gate, state vector, or POVM label.
 set_all_parameterizations(self, gate_type, prep_type='auto', povm_type='auto', instrument_type='auto', extra=None)¶
Convert all gates, states, and POVMs to a specific parameterization type.
 Parameters
parameterization_type (string) –
The gate, state, and POVM parameterization type. Allowed values are (where ‘*’ means ” terms” and ” clifford terms” evolutiontype suffixes are allowed):
”full” : each gate / state / POVM effect element is an independent parameter
”TP” : TracePreserving gates and state preps
”static” : no parameters
”static unitary” : no parameters; convert superops to unitaries
”clifford” : no parameters; convert unitaries to Clifford symplecitics.
”GLND*” : General unconstrained Lindbladian
”CPTP*” : CompletelyPositiveTracePreserving
”H+S+A*” : Hamiltoian, PauliStochastic, and Affine errors
”H+S*” : Hamiltonian and PauliStochastic errors
”S+A*” : PauliStochastic and Affine errors
”S*” : PauliStochastic errors
”H+D+A*” : Hamiltoian, Depolarization, and Affine errors
”H+D*” : Hamiltonian and Depolarization errors
”D+A*” : Depolarization and Affine errors
”D*” : Depolarization errors
Any of the above with “S” replaced with “s” or “D” replaced with “d”. This removes the CPTP constraint on the gates and SPAM operations (and as such is seldom used).
extra (dict, optional) – For “H+S terms” type, this may specify a dictionary of unitary gates and pure state vectors to be used as the ideal operation of each gate/SPAM operation.
 Returns
None
 __setstate__(self, state_dict)¶
 property num_elements(self)¶
Return the number of total operation matrix and spam vector elements in this model.
This is in general different from the number of parameters in the model, which are the number of free variables used to generate all of the matrix and vector elements.
 Returns
int – the number of model elements.
 property num_nongauge_params(self)¶
Return the number of nongauge parameters in this model.
 Returns
int – the number of nongauge model parameters.
 property num_gauge_params(self)¶
Return the number of gauge parameters in this model.
 Returns
int – the number of gauge model parameters.
 deriv_wrt_params(self)¶
The elementwise derivative of all this models’ operations.
Constructs a matrix whose columns are the vectorized derivatives of all the model’s raw matrix and vector elements (placed in a vector) with respect to each single model parameter.
Thus, each column has length equal to the number of elements in the model, and there are num_params() columns. In the case of a “fully parameterized model” (i.e. all operation matrices and SPAM vectors are fully parameterized) then the resulting matrix will be the (square) identity matrix.
 Returns
numpy array – 2D array of derivatives.
 compute_nongauge_projector(self, item_weights=None, non_gauge_mix_mx=None)¶
Construct a projector onto the nongauge parameter space.
Useful for isolating the gauge degrees of freedom from the nongauge degrees of freedom.
 Parameters
item_weights (dict, optional) – Dictionary of weighting factors for individual gates and spam operators. Keys can be gate, state preparation, POVM effect, spam labels, or the special strings “gates” or “spam” whic represent the entire set of gate or SPAM operators, respectively. Values are floating point numbers. These weights define the metric used to compute the nongauge space, orthogonal the gauge space, that is projected onto.
non_gauge_mix_mx (numpy array, optional) – An array of shape (n_non_gauge_params,n_gauge_params) specifying how to mix the nongauge degrees of freedom into the gauge degrees of freedom that are projected out by the returned object. This argument essentially sets the offdiagonal block of the metric used for orthogonality in the “gauge + nongauge” space. It is for advanced usage and typically left as None (the default).
 Returns
numpy array – The projection operator as a N x N matrix, where N is the number of parameters (obtained via num_params()). This projector acts on parameterspace, and has rank equal to the number of nongauge degrees of freedom.
 transform_inplace(self, s)¶
Gauge transform this model.
Update each of the operation matrices G in this model with inv(s) * G * s, each rhoVec with inv(s) * rhoVec, and each EVec with EVec * s
 Parameters
s (GaugeGroupElement) – A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
 Returns
None
 frobeniusdist(self, other_model, transform_mx=None, item_weights=None, normalize=True)¶
Compute the weighted frobenius norm of the difference between this model and other_model.
Differences in each corresponding gate matrix and spam vector element are squared, weighted (using item_weights as applicable), then summed. The value returned is the square root of this sum, or the square root of this sum divided by the number of summands if normalize == True.
 Parameters
other_model (Model) – the other model to difference against.
transform_mx (numpy array, optional) – if not None, transform this model by G => inv(transform_mx) * G * transform_mx, for each operation matrix G (and similar for rho and E vectors) before taking the difference. This transformation is applied only for the difference and does not alter the values stored in this model.
item_weights (dict, optional) – Dictionary of weighting factors for individual gates and spam operators. Weights are applied multiplicatively to the squared differences, i.e., (before the final square root is taken). Keys can be gate, state preparation, POVM effect, or spam labels, as well as the two special labels “gates” and “spam” which apply to all of the gate or SPAM elements, respectively (but are overridden by specific element values). Values are floating point numbers. By default, all weights are 1.0.
normalize (bool, optional) – if True (the default), the sum of weighted squareddifferences is divided by the weighted number of differences before the final square root is taken. If False, the division is not performed.
 Returns
float
 residuals(self, other_model, transform_mx=None, item_weights=None)¶
Compute the weighted residuals between two models.
Residuals are the differences in corresponding operation matrix and spam vector elements.
 Parameters
other_model (Model) – the other model to difference against.
transform_mx (numpy array, optional) – if not None, transform this model by G => inv(transform_mx) * G * transform_mx, for each operation matrix G (and similar for rho and E vectors) before taking the difference. This transformation is applied only for the difference and does not alter the values stored in this model.
item_weights (dict, optional) – Dictionary of weighting factors for individual gates and spam operators. Weights applied such that they act multiplicatively on the squared differences, so that the residuals themselves are scaled by the square roots of these weights. Keys can be gate, state preparation, POVM effect, or spam labels, as well as the two special labels “gates” and “spam” which apply to all of the gate or SPAM elements, respectively (but are overridden by specific element values). Values are floating point numbers. By default, all weights are 1.0.
 Returns
residuals (numpy.ndarray) – A 1D array of residuals (differences w.r.t. other)
nSummands (int) – The (weighted) number of elements accounted for by the residuals.
 jtracedist(self, other_model, transform_mx=None, include_spam=True)¶
Compute the Jamiolkowski trace distance between this model and other_model.
This is defined as the maximum of the trace distances between each corresponding gate, including spam gates.
 Parameters
other_model (Model) – the other model to difference against.
transform_mx (numpy array, optional) – if not None, transform this model by G => inv(transform_mx) * G * transform_mx, for each operation matrix G (and similar for rho and E vectors) before taking the difference. This transformation is applied only for the difference and does not alter the values stored in this model.
include_spam (bool, optional) – Whether to add to the maxtracedistance the frobenius distances between corresponding SPAM operations.
 Returns
float
 diamonddist(self, other_model, transform_mx=None, include_spam=True)¶
Compute the diamondnorm distance between this model and other_model.
This is defined as the maximum of the diamondnorm distances between each corresponding gate, including spam gates.
 Parameters
other_model (Model) – the other model to difference against.
transform_mx (numpy array, optional) – if not None, transform this model by G => inv(transform_mx) * G * transform_mx, for each operation matrix G (and similar for rho and E vectors) before taking the difference. This transformation is applied only for the difference and does not alter the values stored in this model.
include_spam (bool, optional) – Whether to add to the maxdiamonddistance the frobenius distances between corresponding SPAM operations.
 Returns
float
 _tpdist(self)¶
Compute the “distance” between this model and the space of tracepreserving (TP) maps.
This is defined as the square root of the sumofsquared deviations among the first row of all operation matrices and the first element of all state preparations.
 Returns
float
 strdiff(self, other_model, metric='frobenius')¶
Return a string describing the distances between this model and other_model.
The returned string displays differences between each corresponding gate, state prep, and POVM effect.
 Parameters
other_model (Model) – the other model to difference against.
metric ({'frobenius', 'infidelity', 'diamond'}) – Which distance metric to use.
 Returns
str
 _init_copy(self, copy_into, memo)¶
Copies any “tricky” member of this model into copy_into, before deep copying everything else within a .copy() operation.
 __str__(self)¶
Return str(self).
 all_objects(self)¶
Iterate over all of the (label, operator object) entities in this model.
This iterator runs over all state preparations, POVMS, operations, and instruments.
 depolarize(self, op_noise=None, spam_noise=None, max_op_noise=None, max_spam_noise=None, seed=None)¶
Apply depolarization uniformly or randomly to this model’s gate and/or SPAM elements.
The result is returned without modifying the original (this) model. You must specify either op_noise or max_op_noise (for the amount of gate depolarization), and either spam_noise or max_spam_noise (for spam depolarization).
 Parameters
op_noise (float, optional) –
 apply depolarizing noise of strength
1op_noise
to all gates in the model. (Multiplies each assumedPaulibasis operation matrix by the diagonal matrix with
(1.0op_noise)
along all the diagonal elements except the first (the identity).
 apply depolarizing noise of strength
spam_noise (float, optional) – apply depolarizing noise of strength
1spam_noise
to all SPAM opeations (state and POVM effects) in the model. (Multiplies the nonidentity part of each assumedPaulibasis state preparation vector and measurement vector by(1.0spam_noise)
).max_op_noise (float, optional) – specified instead of op_noise; apply a random depolarization with maximum strength
1max_op_noise
to each gate in the model.max_spam_noise (float, optional) – specified instead of spam_noise; apply a random depolarization with maximum strength
1max_spam_noise
to each state preparation and POVM in the model.seed (int, optional) – if not
None
, seed numpy’s random number generator with this value before generating random depolarizations.
 Returns
Model – the depolarized Model
 rotate(self, rotate=None, max_rotate=None, seed=None)¶
Apply a rotation uniformly or randomly to this model.
Uniformly means the same rotation applied to each gate and randomly means different random rotations are applied to each gate of this model. The result is returned without modifying the original (this) model.
You must specify either rotate or max_rotate. This method currently only works on nqubit models.
 Parameters
rotate (tuple of floats, optional) – If you specify the rotate argument, then the same rotation operation is applied to each gate. That is, each gate’s matrix G is composed with a rotation operation R (so G > dot(R, G) ) where R is the unitary superoperator corresponding to the unitary operator U = exp( sum_k( i * rotate[k] / 2.0 * Pauli_k ) ). Here Pauli_k ranges over all of the nonidentity unnormalized Pauli operators (e.g. {X,Y,Z} for 1 qubit, {IX, IY, IZ, XI, XX, XY, XZ, YI, YX, YY, YZ, ZI, ZX, ZY, ZZ} for 2 qubits).
max_rotate (float, optional) – If max_rotate is specified (instead of rotate), then pyGSTi randomly generates a different rotate tuple, and applies the corresponding rotation, to each gate in this Model. Each component of each tuple is drawn uniformly from [0, max_rotate).
seed (int, optional) – if not None, seed numpy’s random number generator with this value before generating random depolarizations.
 Returns
Model – the rotated Model
 randomize_with_unitary(self, scale, seed=None, rand_state=None)¶
Create a new model with random unitary perturbations.
Apply a random unitary to each element of a model, and return the result, without modifying the original (this) model. This method works on Model as long as the dimension is a perfect square.
 Parameters
scale (float) – maximum element magnitude in the generator of each random unitary transform.
seed (int, optional) – if not None, seed numpy’s random number generator with this value before generating random depolarizations.
rand_state (numpy.random.RandomState) – A RandomState object to generate samples from. Can be useful to set instead of seed if you want reproducible distribution samples across multiple random function calls but you don’t want to bother with manually incrementing seeds between those calls.
 Returns
Model – the randomized Model
 increase_dimension(self, new_dimension)¶
Enlarge the dimension of this model.
Enlarge the spam vectors and operation matrices of model to a specified dimension, and return the resulting inflated model. Spam vectors are zeropadded and operation matrices are padded with 1’s on the diagonal and zeros on the offdiagonal (effectively padded by identity operation).
 Parameters
new_dimension (int) – the dimension of the returned model. That is, the returned model will have rho and E vectors that have shape (new_dimension,1) and operation matrices with shape (new_dimension,new_dimension)
 Returns
Model – the increaseddimension Model
 _decrease_dimension(self, new_dimension)¶
Decrease the dimension of this model.
Shrink the spam vectors and operation matrices of model to a specified dimension, and return the resulting model.
 Parameters
new_dimension (int) – the dimension of the returned model. That is, the returned model will have rho and E vectors that have shape (new_dimension,1) and operation matrices with shape (new_dimension,new_dimension)
 Returns
Model – the decreaseddimension Model
 kick(self, absmag=1.0, bias=0, seed=None)¶
“Kick” this model by adding to each gate a random matrix.
The random matrices have values uniformly distributed in the interval [biasabsmag,bias+absmag].
 Parameters
absmag (float, optional) – The maximum magnitude of the entries in the “kick” matrix relative to bias.
bias (float, optional) – The bias of the entries in the “kick” matrix.
seed (int, optional) – if not None, seed numpy’s random number generator with this value before generating random depolarizations.
 Returns
Model – the kicked model.
 compute_clifford_symplectic_reps(self, oplabel_filter=None)¶
Constructs a dictionary of the symplectic representations for all the Clifford gates in this model.
Non
StaticCliffordOp
gates will be ignored and their entries omitted from the returned dictionary. Parameters
oplabel_filter (iterable, optional) – A list, tuple, or set of operation labels whose symplectic representations should be returned (if they exist).
 Returns
dict – keys are operation labels and/or just the root names of gates (without any state space indices/labels). Values are (symplectic_matrix, phase_vector) tuples.
 print_info(self)¶
Print to stdout relevant information about this model.
This information includes the Choi matrices and their eigenvalues.
 Returns
None
 _effect_labels_for_povm(self, povm_lbl)¶
Gets the effect labels corresponding to the possible outcomes of POVM label povm_lbl.
 Parameters
povm_lbl (Label) – POVM label.
 Returns
list – A list of strings which label the POVM outcomes.
 _member_labels_for_instrument(self, inst_lbl)¶
Gets the member labels corresponding to the possible outcomes of the instrument labeled by inst_lbl.
 Parameters
inst_lbl (Label) – Instrument label.
 Returns
list – A list of strings which label the instrument members.
 _reinit_opcaches(self)¶
Called when parameter vector structure changes and self._opcaches should be cleared & reinitialized
 create_processor_spec(self, qubit_labels='auto')¶
Create a processor specification from this model with the given qubit labels.
Currently this only works for models on qubits.
 Parameters
qubit_labels (tuple or “auto”, optional) – A tuple of qubit labels, e.g. (‘Q0’, ‘Q1’) or (0, 1). “auto” uses the labels in this model’s state space labels.
 Returns
QubitProcessorSpec
 create_modelmember_graph(self)¶
Generate a ModelMemberGraph for the model.
 Returns
ModelMemberGraph – A directed graph capturing dependencies among model members
 _to_nice_serialization(self)¶
 classmethod _from_nice_serialization(cls, state)¶
 errorgen_coefficients(self, normalized_elem_gens=True)¶
TODO: docstring  returns a nested dict containing all the error generator coefficients for all the operations in this model.
 _add_reparameterization(self, primitive_op_labels, fogi_dirs, errgenset_space_labels)¶
 _format_gauge_action_matrix(self, mx, op, reduce_to_model_space, row_basis, op_gauge_basis, create_complete_basis_fn)¶
 setup_fogi(self, initial_gauge_basis, create_complete_basis_fn=None, op_label_abbrevs=None, reparameterize=False, reduce_to_model_space=True, dependent_fogi_action='drop')¶
 fogi_errorgen_component_labels(self, include_fogv=False, typ='normal')¶
 fogi_errorgen_components_array(self, include_fogv=False, normalized_elem_gens=True)¶
 set_fogi_errorgen_components_array(self, components, include_fogv=False, normalized_elem_gens=True, truncate=False)¶
 class pygsti.report._StateSpace¶
Bases:
pygsti.baseobjs.nicelyserializable.NicelySerializable
Base class for defining a state space (Hilbert or HilbertSchmidt space).
This base class just sets the API for a “state space” in pyGSTi, accessed as the direct sum of one or more tensor products of Hilbert spaces.
 classmethod cast(cls, obj)¶
Casts obj into a
StateSpace
object if possible.If obj is already of this type, it is simply returned without modification.
 Parameters
obj (StateSpace or int or list) – Either an alreadybuilt state space object or an integer specifying the number of qubits, or a list of labels as would be provided to the first argument of :method:`ExplicitStateSpace.__init__`.
 Returns
StateSpace
 property udim(self)¶
Integer Hilbert (unitary operator) space dimension of this quantum state space.
Raises an error if this space is not a quantum state space.
 property dim(self)¶
Integer HilbertSchmidt (superoperator) or classical dimension of this state space.
 property num_qubits(self)¶
The number of qubits in this quantum state space.
Raises a ValueError if this state space doesn’t consist entirely of qubits.
 property num_tensor_product_blocks(self)¶
The number of tensorproduct blocks which are directsummed to get the final state space.
 Returns
int
 property tensor_product_blocks_labels(self)¶
The labels for all the tensorproduct blocks.
 Returns
tuple of tuples
 property tensor_product_blocks_dimensions(self)¶
The superoperator dimensions for all the tensorproduct blocks.
 Returns
tuple of tuples
 property tensor_product_blocks_udimensions(self)¶
The unitary operator dimensions for all the tensorproduct blocks.
 Returns
tuple of tuples
 property tensor_product_blocks_types(self)¶
The type (quantum vs classical) of all the tensorproduct blocks.
 Returns
tuple of tuples
 abstract label_dimension(self, label)¶
The superoperator dimension of the given label (from any tensor product block)
 Parameters
label (str or int) – The label whose dimension should be retrieved.
 Returns
int
 abstract label_udimension(self, label)¶
The unitary operator dimension of the given label (from any tensor product block)
 Parameters
label (str or int) – The label whose dimension should be retrieved.
 Returns
int
 abstract label_tensor_product_block_index(self, label)¶
The index of the tensor product block containing the given label.
 Parameters
label (str or int) – The label whose index should be retrieved.
 Returns
int
 abstract label_type(self, label)¶
The type (quantum or classical) of the given label (from any tensor product block).
 Parameters
label (str or int) – The label whose type should be retrieved.
 Returns
str
 tensor_product_block_labels(self, i_tpb)¶
The labels for the iTBPth tensorproduct block.
 Parameters
i_tpb (int) – Tensorproduct block index.
 Returns
tuple
 tensor_product_block_dimensions(self, i_tpb)¶
The superoperator dimensions for the factors in the iTBPth tensorproduct block.
 Parameters
i_tpb (int) – Tensorproduct block index.
 Returns
tuple
 tensor_product_block_udimensions(self, i_tpb)¶
The unitaryoperator dimensions for the factors in the iTBPth tensorproduct block.
 Parameters
i_tpb (int) – Tensorproduct block index.
 Returns
tuple
 copy(self)¶
Return a copy of this StateSpace.
 Returns
StateSpace
 is_compatible_with(self, other_state_space)¶
Whether another state space is compatible with this one.
Two state spaces are considered “compatible” when their overall dimensions agree (even if their tensor product block structure and labels do not). (This checks whether the Hilbert spaces are isomorphic.)
 Parameters
other_state_space (StateSpace) – The state space to check compatibility with.
 Returns
bool
 property is_entirely_qubits(self)¶
Whether this state space is just the tensor product of qubit subspaces.
 Returns
bool
 is_entire_space(self, labels)¶
True if this state space is a single tensor product block with (exactly, in order) the given set of labels.
 Parameters
labels (iterable) – the labels to test.
 Returns
bool
 contains_labels(self, labels)¶
True if this state space contains all of a given set of labels.
 Parameters
labels (iterable) – the labels to test.
 Returns
bool
 contains_label(self, label)¶
True if this state space contains a given label.
 Parameters
label (str or int) – the label to test for.
 Returns
bool
 property common_dimension(self)¶
Returns the common superop dimension of all the labels in this space.
If not all the labels in this space have the same dimension, then None is returned to indicate this.
This property is useful when working with stencils, where operations are created for a “stencil space” that is not exactly a subspace of a StateSpace space but will be mapped to one in the future.
 Returns
int or None
 property common_udimension(self)¶
Returns the common unitaryop dimension of all the labels in this space.
If not all the labels in this space have the same dimension, then None is returned to indicate this.
This property is useful when working with stencils, where operations are created for a “stencil space” that is not exactly a subspace of a StateSpace space but will be mapped to one in the future.
 Returns
int or None
 create_subspace(self, labels)¶
Create a subStateSpace object from a set of existing labels.
 Parameters
labels (iterable) – The labels to include in the returned state space.
 Returns
StateSpace
 intersection(self, other_state_space)¶
Create a state space whose labels are the intersection of the labels of this space and one other.
Dimensions associated with the labels are preserved, as is the ordering of tensor product blocks. If the two spaces have the same label, but their dimensions or indices do not agree, an error is raised.
 Parameters
other_state_space (StateSpace) – The other state space.
 Returns
StateSpace
 union(self, other_state_space)¶
Create a state space whose labels are the union of the labels of this space and one other.
Dimensions associated with the labels are preserved, as is the tensor product block index. If the two spaces have the same label, but their dimensions or indices do not agree, an error is raised.
 Parameters
other_state_space (StateSpace) – The other state space.
 Returns
StateSpace
 create_stencil_subspace(self, labels)¶
Create a template subStateSpace object from a set of potentially stenciltype labels.
That is, the elements of labels don’t need to actually exist within this state space – they may be stencil labels that will resolve to a label in this state space later on.
 Parameters
labels (iterable) – The labels to include in the returned state space.
 Returns
StateSpace
 __repr__(self)¶
Return repr(self).
 __hash__(self)¶
Return hash(self).
 __eq__(self, other_statespace)¶
Return self==value.
 class pygsti.report._Circuit(layer_labels=(), line_labels='auto', num_lines=None, editable=False, stringrep=None, name='', check=True, expand_subcircuits='default', occurrence=None, compilable_layer_indices=None)¶
Bases:
object
A quantum circuit.
A Circuit represents a quantum circuit, consisting of state preparation, gates, and measurement operations. It is composed of some number of “lines”, typically one per qubit, and stores the operations on these lines as a sequence of
Label
objects, one per circuit layer, whose .sslbls members indicate which line(s) the label belongs on. When a circuit is created with ‘editable=True’, a rich set of operations may be used to construct the circuit in place, after which done_editing() should be called so that the Circuit can be properly hashed as needed. Parameters
layer_labels (iterable of Labels or str) –
This argument provides a list of the layer labels specifying the state preparations, gates, and measurements for the circuit. This argument can also be a
Circuit
or a string, in which case it is parsed as a textformatted circuit. Internally this will eventually be converted to a list of Label objects, one per layer, but it may be specified using anything that can be readily converted to a Label objects. For example, any of the following are allowed:[‘Gx’,’Gx’] : X gate on each of 2 layers
`[Label(‘Gx’),Label(‘Gx’)] : same as above
[(‘Gx’,0),(‘Gy’,0)] : X then Y on qubit 0 (2 layers)
[[(‘Gx’,0),(‘Gx’,1)],[(‘Gy’,0),(‘Gy’,1)]] : parallel X then Y on qubits 0 & 1
line_labels (iterable, optional) – The (string valued) label for each circuit line. If ‘auto’, then line_labels is taken to be the list of all statespace labels present within layer_labels. If there are no such labels (e.g. if layer_labels contains just gate names like (‘Gx’,’Gy’)), then the special value ‘*’ is used as a single line label.
num_lines (int, optional) – Specify this instead of line_labels to set the latter to the integers between 0 and num_lines1.
editable (bool, optional) – Whether the created Circuit is created in able to be modified. If True, then you should call done_editing() once the circuit is completely assembled, as this makes the circuit readonly and allows it to be hashed.
stringrep (string, optional) – A string representation for the circuit. If None (the default), then this will be generated automatically when needed. One reason you’d want to specify this is if you know of a nice compact string representation that you’d rather use, e.g. “Gx^4” instead of the automatically generated “GxGxGxGx”. If you want to initialize a Circuit entirely from a string representation you can either specify the string in as layer_labels or set layer_labels to None and stringrep to any valid (oneline) circuit string.
name (str, optional) – A name for this circuit (useful if/when used as a block within larger circuits).
check (bool, optional) – Whether stringrep should be checked against layer_labels to ensure they are consistent, and whether the labels in layer_labels are a subset of line_labels. The only reason you’d want to set this to False is if you’re absolutely sure stringrep and line_labels are consistent and want to save computation time.
expand_subcircuits (bool or "default") – If “default”, then the value of Circuit.default_expand_subcircuits is used. If True, then any subcircuits (e.g. anything exponentiated like “(GxGy)^4”) will be expanded when it is stored within the created Circuit. If False, then such subcircuits will be left asis. It’s typically more robust to expand subcircuits as this facilitates comparison (e.g. so “GxGx” == “Gx^2”), but in cases when you have massive exponents (e.g. “Gx^8192”) it may improve performance to set expand_subcircuits=False.
occurrence (hashable, optional) – A value to set as the “occurrence id” for this circuit. This value doesn’t affect the circuit an any way except by affecting it’s hashing and equivalence testing. Circuits with different occurrence ids are not equivalent. Occurrence values effectively allow multiple copies of the same ciruit to be stored in a dictionary or
DataSet
.compilable_layer_indices (tuple, optional) – The circuitlayer indices that may be internally altered (but retaining the same target operation) and/or combined with the following circuit layer by a hardware compiler.when executing this circuit. Layers that are not “compilable” are effectively followed by a barrier which prevents the hardward compiler from restructuring the circuit across the layer boundary.
 default_expand_subcircuits¶
By default, expand subcircuit labels.
 Type
bool
 line_labels¶
The line labels (often qubit labels) of this circuit.
 Type
tuple
 layertup¶
This Circuit’s layers as a standard Python tuple of layer Labels.
 Type
tuple
 tup¶
This Circuit as a standard Python tuple of layer Labels and line labels.
 Type
tuple
 str¶
The Python string representation of this Circuit.
 Type
str
 default_expand_subcircuits = True¶
 classmethod cast(cls, obj)¶
Convert obj into a
Circuit
. Parameters
obj (object) – Object to convert
 Returns
Circuit
 classmethod from_tuple(cls, tup)¶
Creates a
Circuit
from a tuple Parameters
tup (tuple) – The tuple to convert.
 Returns
Circuit
 classmethod _fastinit(cls, labels, line_labels, editable, name='', stringrep=None, occurrence=None, compilable_layer_indices=None)¶
 _bare_init(self, labels, line_labels, editable, name='', stringrep=None, occurrence=None, compilable_layer_indices=None)¶
 to_label(self, nreps=1)¶
Construct and return this entire circuit as a
CircuitLabel
.Note: occurrenceid information is not stored in a circuit label, so circuits that differ only in their occurence_id will return circuit labels that are equal.
 Parameters
nreps (int, optional) – The number of times this circuit will be repeated (CircuitLabels support exponentiation and you can specify this here).
 Returns
CircuitLabel
 property line_labels(self)¶
The line labels (often qubit labels) of this circuit.
 property name(self)¶
The name of this circuit.
Note: the name is not a part of the hashed value. The name is used to name the
CircuitLabel
returned from :method:`to_label`.
 property occurrence(self)¶
The occurrence id of this circuit.
 property layertup(self)¶
This Circuit’s layers as a standard Python tuple of layer Labels.
 Returns
tuple
 property tup(self)¶
This Circuit as a standard Python tuple of layer Labels and line labels.
 Returns
tuple
 property compilable_layer_indices(self)¶
Tuple of the layer indices corresponding to “compilable” layers.
 property compilable_by_layer(self)¶
Boolean array indicating whether each layer is “compilable” or not.
 property str(self)¶
The Python string representation of this Circuit.
 Returns
str
 property layerstr(self)¶
Just the string representation of the circuit layers (no ‘@<line_labels>’ suffix)
 property linesstr(self)¶
Just the string representation of the circuit’s line labels (the ‘@<line_labels>’ suffix)
 _labels_lines_str(self)¶
Split the string representation up into layerlabels & linelabels parts
 __hash__(self)¶
Return hash(self).
 __len__(self)¶
 __iter__(self)¶
 __contains__(self, x)¶
Note: this is not covered by __iter__ for case of contained CircuitLabels
 __radd__(self, x)¶
 __add__(self, x)¶
 repeat(self, ntimes, expand='default')¶
Repeat this circuit ntimes times.
 Parameters
ntimes (int) – Number of repetitions.
expand (bool or "default", optional) – When False, the returned circuit will contain a
CircuitLabel
encapsulating the repetitions without explicitly storing them. When True, the returned circuit will be expanded into the ntimes repetitions. “default” means to use the value in the class variable Circuit.default_expand_subcircuits.
 __mul__(self, x)¶
 __pow__(self, x)¶
 __eq__(self, x)¶
Return self==value.
 __lt__(self, x)¶
Return self<value.
 __gt__(self, x)¶
Return self>value.
 property num_lines(self)¶
The number of lines in this circuit.
 Returns
int
 copy(self, editable='auto')¶
Returns a copy of the circuit.
 Parameters
editable ({True,False,"auto"}) – Whether returned copy is editable. If “auto” is given, then the copy is editable if and only if this Circuit is.
 Returns
Circuit
 clear(self)¶
Removes all the gates in a circuit (preserving the number of lines).
 Returns
None
 _proc_layers_arg(self, layers)¶
Preprocess the layers argument used by many methods
 _proc_lines_arg(self, lines)¶
Preprocess the lines argument used by many methods
 _proc_key_arg(self, key)¶
Preprocess the key argument used by many methods
 _layer_components(self, ilayer)¶
Get the components of the ilayerth layer as a list/tuple.
 _remove_layer_component(self, ilayer, indx)¶
Removes the indxth component from the ilayerth layer
 _append_layer_component(self, ilayer, val)¶
Add val to the ilayerth layer
 _replace_layer_component(self, ilayer, indx, val)¶
 extract_labels(self, layers=None, lines=None, strict=True)¶
Get a subregion  a “rectangle”  of this Circuit.
This can be used to select multiple layers and/or lines of this Circuit. The strict argument controls whether gates need to be entirely within the given rectangle or can be intersecting it. If layers is a single integer then a
Label
is returned (representing a layer or a part of a layer), otherwise aCircuit
is returned. Parameters
layers (int, slice, or list/tuple of ints) – Which layers to select (the horizontal dimension of the selection rectangle). Layers are always selected by index, and this argument can be a single (integer) index  in which case a Label is returned  or multiple indices as given by a slice or list  in which case a Circuit is returned. Note that, even though we speak of a “rectangle”, layer indices do not need to be contiguous. The special value None selects all layers.
lines (str/int, slice, or list/tuple of strs/ints) – Which lines to select (the vertical dimension of the selection rectangle). Lines are selected by their linelabels (elements of the circuit’s .line_labels property), which can be strings and/or integers. A single or multiple linelabels can be specified. If the line labels are integers a slice can be used, otherwise a list or tuple of labels is the only way to select multiple of them. Note that linelabels do not need to be contiguous. The special value None selects all lines.
strict (bool, optional) – When True, only gates lying completely within the selected region are included in the return value. If a gate straddles the region boundary (e.g. if we select just line 1 and the circuit contains “Gcnot:1:2”) then it is silently notincluded in the returned label or circuit. If False, then gates which straddle the region boundary are included. Note that this may result in a Label or Circuit containing more line labels than where requested in the call to extract_labels(…)..
 Returns
Label or Circuit – The requested portion of this circuit, given as a Label if layers is a single integer and as a Circuit otherwise. Note: if you want a Circuit when only selecting one layer, set layers to a slice or tuple containing just a single index.
 set_labels(self, lbls, layers=None, lines=None)¶
Write lbls to the block defined by the layers and lines arguments.
Note that lbls can be anything interpretable as a
Label
or list of labels. Parameters
lbls (Label, list/tuple of Labels, or Circuit) – When layers is a single integer, lbls should be a single “layer label” of type Label. Otherwise, lbls should be a list or tuple of Label objects with length equal to the number of layers being set. A Circuit may also be used in this case.
layers (int, slice, or list/tuple of ints) – Which layers to set (the horizontal dimension of the destination rectangle). Layers are always selected by index, and this argument can be a single (integer) index or multiple indices as given by a slice or list. Note that these indices do not need to be contiguous. The special value None stands for all layers.
lines (str/int, slice, or list/tuple of strs/ints) – Which lines to set (the vertical dimension of the destination rectangle). Lines are selected by their linelabels, which can be strings and/or integers. A single or multiple linelabels can be specified. If the line labels are integers a slice can be used, otherwise a list or tuple of labels is the only way to specify multiple of them. The linelabels do not need to be contiguous. The special value None stands for all lines, and in this case new lines will be created if there are new statespace labels in lbls (when lines is not None an error is raised instead).
 Returns
None
 insert_idling_layers(self, insert_before, num_to_insert, lines=None)¶
Inserts into this circuit one or more idling (blank) layers.
By default, complete layer(s) are inserted. The lines argument allows you to insert partial layers (on only a subset of the lines).
 Parameters
insert_before (int) – The layer index to insert the new layers before. Can be from 0 (insert at the beginning) to len(self)1 (insert at end), and negative indexing can be used to insert relative to the last layer. The special value None inserts at the end.
num_to_insert (int) – The number of new layers to insert.
lines (str/int, slice, or list/tuple of strs/ints, optional) – Which lines should have new layers (blank circuit space) inserted into them. A single or multiple linelabels can be specified, similarly as in :method:`extract_labels`. The default value None stands for all lines.
 Returns
Circuit
 insert_idling_layers_inplace(self, insert_before, num_to_insert, lines=None)¶
Inserts into this circuit one or more idling (blank) layers.
By default, complete layer(s) are inserted. The lines argument allows you to insert partial layers (on only a subset of the lines).
 Parameters
insert_before (int) – The layer index to insert the new layers before. Can be from 0 (insert at the beginning) to len(self)1 (insert at end), and negative indexing can be used to insert relative to the last layer. The special value None inserts at the end.
num_to_insert (int) – The number of new layers to insert.
lines (str/int, slice, or list/tuple of strs/ints, optional) – Which lines should have new layers (blank circuit space) inserted into them. A single or multiple linelabels can be specified, similarly as in :method:`extract_labels`. The default value None stands for all lines.
 Returns
None
 _append_idling_layers_inplace(self, num_to_insert, lines=None)¶
Adds one or more idling (blank) layers to the end of this circuit.
By default, complete layer(s) are appended. The lines argument allows you to add partial layers (on only a subset of the lines).
 Parameters
num_to_insert (int) – The number of new layers to append.
lines (str/int, slice, or list/tuple of strs/ints, optional) – Which lines should have new layers (blank circuit space) inserted into them. A single or multiple linelabels can be specified, similarly as in :method:`extract_labels`. The default value None stands for all lines.
 Returns
None
 insert_labels_into_layers(self, lbls, layer_to_insert_before, lines=None)¶
Inserts into this circuit the contents of lbls into new full or partial layers.
By default, complete layer(s) are inserted. The lines argument allows you to insert partial layers (on only a subset of the lines).
 Parameters
lbls (list/tuple of Labels, or Circuit) – The full or partial layer labels to insert. The length of this list, tuple, or circuit determines the number of layers which are inserted.
layer_to_insert_before (int) – The layer index to insert lbls before. Can be from 0 (insert at the beginning) to len(self)1 (insert at end), and negative indexing can be used to insert relative to the last layer. The special value None inserts at the end.
lines (str/int, slice, or list/tuple of strs/ints, optional) – Which lines should have lbls inserted into them. Currently this can only be a larger set than the set of line labels present in lbls (in future versions this may allow filtering of lbls). value None stands for all lines.
 Returns
Circuit
 insert_labels_into_layers_inplace(self, lbls, layer_to_insert_before, lines=None)¶
Inserts into this circuit the contents of lbls into new full or partial layers.
By default, complete layer(s) are inserted. The lines argument allows you to insert partial layers (on only a subset of the lines).
 Parameters
lbls (list/tuple of Labels, or Circuit) – The full or partial layer labels to insert. The length of this list, tuple, or circuit determines the number of layers which are inserted.
layer_to_insert_before (int) – The layer index to insert lbls before. Can be from 0 (insert at the beginning) to len(self)1 (insert at end), and negative indexing can be used to insert relative to the last layer. The special value None inserts at the end.
lines (str/int, slice, or list/tuple of strs/ints, optional) – Which lines should have lbls inserted into them. Currently this can only be a larger set than the set of line labels present in lbls (in future versions this may allow filtering of lbls). value None stands for all lines.
 Returns
None
 insert_idling_lines(self, insert_before, line_labels)¶
Insert one or more idling (blank) lines into this circuit.
 Parameters
insert_before (str or int) – The line label to insert new lines before. The special value None inserts lines at the bottom of this circuit.
line_labels (list or tuple) – A list or tuple of the new line labels to insert (can be integers and/or strings).
 Returns
Circuit
 insert_idling_lines_inplace(self, insert_before, line_labels)¶
Insert one or more idling (blank) lines into this circuit.
 Parameters
insert_before (str or int) – The line label to insert new lines before. The special value None inserts lines at the bottom of this circuit.
line_labels (list or tuple) – A list or tuple of the new line labels to insert (can be integers and/or strings).
 Returns
None
 _append_idling_lines(self, line_labels)¶
Add one or more idling (blank) lines onto the bottom of this circuit.
 Parameters
line_labels (list or tuple) – A list or tuple of the new line labels to insert (can be integers and/or strings).
 Returns
None
 insert_labels_as_lines_inplace(self, lbls, layer_to_insert_before=None, line_to_insert_before=None, line_labels='auto')¶
Inserts into this circuit the contents of lbls into new lines.
By default, lbls is inserted at the beginning of the new lines(s). The layer_to_insert_before argument allows you to insert lbls beginning at a layer of your choice.
 Parameters
lbls (list/tuple of Labels, or Circuit) – A list of layer labels to insert as new lines. The statespace (line) labels within lbls must not overlap with that of this circuit or an error is raised. If lbls contains more layers than this circuit currently has, new layers are added automatically.
layer_to_insert_before (int) – The layer index to insert lbls before. Can be from 0 (insert at the beginning) to len(self)1 (insert at end), and negative indexing can be used to insert relative to the last layer. The default value of None inserts at the beginning.
line_to_insert_before (str or int) – The line label to insert the new lines before. The default value of None inserts lines at the bottom of the circuit.
line_labels (list, tuple, or "auto") – The labels of the new lines being inserted. If “auto”, then these are inferred from lbls.
 Returns
None
 insert_labels_as_lines(self, lbls, layer_to_insert_before=None, line_to_insert_before=None, line_labels='auto')¶
Inserts into this circuit the contents of lbls into new lines.
By default, lbls is inserted at the beginning of the new lines(s). The layer_to_insert_before argument allows you to insert lbls beginning at a layer of your choice.
 Parameters
lbls (list/tuple of Labels, or Circuit) – A list of layer labels to insert as new lines. The statespace (line) labels within lbls must not overlap with that of this circuit or an error is raised. If lbls contains more layers than this circuit currently has, new layers are added automatically.
layer_to_insert_before (int) – The layer index to insert lbls before. Can be from 0 (insert at the beginning) to len(self)1 (insert at end), and negative indexing can be used to insert relative to the last layer. The default value of None inserts at the beginning.
line_to_insert_before (str or int) – The line label to insert the new lines before. The default value of None inserts lines at the bottom of the circuit.
line_labels (list, tuple, or "auto") – The labels of the new lines being inserted. If “auto”, then these are inferred from lbls.
 Returns
None
 _append_labels_as_lines(self, lbls, layer_to_insert_before=None, line_labels='auto')¶
Adds the contents of lbls as new lines at the bottom of this circuit.
By default, lbls is inserted at the beginning of the new lines(s). The layer_to_insert_before argument allows you to insert lbls beginning at a layer of your choice.
 Parameters
lbls (list/tuple of Labels, or Circuit) – A list of layer labels to append as new lines. The statespace (line) labels within lbls must not overlap with that of this circuit or an error is raised. If lbls contains more layers than this circuit currently has, new layers are added automatically.
layer_to_insert_before (int) – The layer index to insert lbls before. Can be from 0 (insert at the beginning) to len(self)1 (insert at end), and negative indexing can be used to insert relative to the last layer. The default value of None inserts at the beginning.
line_labels (list, tuple, or "auto") – The labels of the new lines being added. If “auto”, then these are inferred from lbls.
 Returns
None
 _clear_labels(self, layers, lines, clear_straddlers=False)¶
remove all labels in a block given by layers and lines Note: layers & lines must be lists/tuples of values; they can’t be slices or single vals
 clear_labels(self, layers=None, lines=None, clear_straddlers=False)¶
Removes all the gates within the given circuit region. Does not reduce the number of layers or lines.
 Parameters
layers (int, slice, or list/tuple of ints) – Defines the horizontal dimension of the region to clear. See :method:`extract_labels` for details.
lines (str/int, slice, or list/tuple of strs/ints) – Defines the vertical dimension of the region to clear. See :method:`extract_labels` for details.
clear_straddlers (bool, optional) – Whether or not gates which straddle cleared and noncleared lines should be cleared. If False and straddling gates exist, an error will be raised.
 Returns
None
 delete_layers(self, layers=None)¶
Deletes one or more layers from the circuit.
 Parameters
layers (int, slice, or list/tuple of ints) – The layer index or indices to delete. See :method:`extract_labels` for details.
 Returns
None
 delete_lines(self, lines, delete_straddlers=False)¶
Deletes one or more lines from the circuit.
 Parameters
lines (str/int, slice, or list/tuple of strs/ints) – The line label(s) to delete. See :method:`extract_labels` for details.
delete_straddlers (bool, optional) – Whether or not gates which straddle deleted and nondeleted lines should be removed. If False and straddling gates exist, an error will be raised.
 Returns
None
 __getitem__(self, key)¶
 __setitem__(self, key, val)¶
 __delitem__(self, key)¶
 to_pythonstr(self, op_labels)¶
Convert this circuit to an “encoded” python string.
In the returned string each operation label is represented as a single character, starting with ‘A’ and continuing down the alphabet. This can be useful for processing operation sequences using python’s string tools (regex in particular).
 Parameters
op_labels (tuple) – An iterable containing at least all the layerLabels that appear in this Circuit, and which will be mapped to alphabet characters, beginning with ‘A’.
 Returns
string – The converted operation sequence.
Examples
(‘Gx’,’Gx’,’Gy’,’Gx’) => “AABA”
 classmethod from_pythonstr(cls, python_string, op_labels)¶
Decode an “encoded string” into a
Circuit
.Create a Circuit from a python string where each operation label is represented as a single character, starting with ‘A’ and continuing down the alphabet. This performs the inverse of :method:`to_pythonstr`.
 Parameters
python_string (string) – string whose individual characters correspond to the operation labels of a operation sequence.
op_labels (tuple) – tuple containing all the operation labels that will be mapped from alphabet characters, beginning with ‘A’.
 Returns
Circuit
Examples
“AABA” => (‘Gx’,’Gx’,’Gy’,’Gx’)
 serialize(self)¶
Serialize the parallel gate operations of this Circuit.
Construct a new Circuit whereby all layers containing multiple gates are converted to separate singlegate layers, effectively putting each elementary gate operation into its own layer. Ordering is dictated by the ordering of the compound layer labels.
 Returns
Circuit
 parallelize(self, can_break_labels=True, adjacent_only=False)¶
Compress a circuit’s gates by performing them in parallel.
Construct a circuit with the same underlying labels as this one, but with as many gates performed in parallel as possible (with some restrictions  see the Parameters section below). Generally, gates are moved as far left (toward the start) of the circuit as possible.
 Parameters
can_break_labels (bool, optional) – Whether compound (parallelgate) labels in this Circuit can be separated during the parallelization process. For example, if can_break_labels=True then “Gx:0[Gy:0Gy:1]” => “[Gx:0Gy:1]Gy:0” whereas if can_break_labels=False the result would remain “Gx:0[Gy:0Gy:1]” because [Gy:0Gy:1] cannot be separated.
adjacent_only (bool, optional) – It True, then operation labels are only allowed to move into an adjacent label, that is, they cannot move “through” other operation labels. For example, if adjacent_only=True then “Gx:0Gy:0Gy:1” => “Gx:0[Gy:0Gy:1]” whereas if adjacent_only=False the result would be “[Gx:0Gy:1]Gy:0. Setting this to True is sometimes useful if you want to parallelize a serial string in such a way that subsequently calling .serialize() will give you back the original string.
 Returns
Circuit
 expand_subcircuits_inplace(self)¶
Expands all
CircuitLabel
labels within this circuit.This operation is done in place and so can only be performed on an editable
Circuit
. Returns
None
 expand_subcircuits(self)¶
Returns a new circuit with
CircuitLabel
labels expanded. Returns
Circuit
 factorize_repetitions_inplace(self)¶
Attempt to replace repeated subcircuits with
CircuitLabel
objects.More or less the reverse of :method:`expand_subcircuits`, this method attempts to collapse repetitions of the same labels into single
CircuitLabel
labels within this circuit.This operation is done in place and so can only be performed on an editable
Circuit
. Returns
None
 insert_layer(self, circuit_layer, j)¶
Inserts a single layer into a circuit.
The input layer does not need to contain a gate that acts on every qubit, but it should not contain more than one gate on a qubit.
 Parameters
circuit_layer (Label) – The layer to insert. A (possibly compound) Label object or something that can be converted into one, e.g. ((‘Gx’,0),(‘Gcnot’,1,2)) or just ‘Gx’.
j (int) – The layer index (depth) at which to insert the circuit_layer.
 Returns
Circuit
 insert_layer_inplace(self, circuit_layer, j)¶
Inserts a single layer into a circuit.
The input layer does not need to contain a gate that acts on every qubit, but it should not contain more than one gate on a qubit.
 Parameters
circuit_layer (Label) – The layer to insert. A (possibly compound) Label object or something that can be converted into one, e.g. ((‘Gx’,0),(‘Gcnot’,1,2)) or just ‘Gx’.
j (int) – The layer index (depth) at which to insert the circuit_layer.
 Returns
None
 insert_circuit(self, circuit, j)¶
Inserts a circuit into this circuit.
The circuit to insert can be over more qubits than this circuit, as long as all qubits that are not part of this circuit are idling. In this case, the idling qubits are all discarded. The circuit to insert can also be on less qubits than this circuit: all other qubits are set to idling. So, the labels of the circuit to insert for all nonidling qubits must be a subset of the labels of this circuit.
 Parameters
circuit (Circuit) – The circuit to be inserted.
j (int) – The layer index (depth) at which to insert the circuit.
 Returns
Circuit
 insert_circuit_inplace(self, circuit, j)¶
Inserts a circuit into this circuit.
The circuit to insert can be over more qubits than this circuit, as long as all qubits that are not part of this circuit are idling. In this case, the idling qubits are all discarded. The circuit to insert can also be on less qubits than this circuit: all other qubits are set to idling. So, the labels of the circuit to insert for all nonidling qubits must be a subset of the labels of this circuit.
 Parameters
circuit (Circuit) – The circuit to be inserted.
j (int) – The layer index (depth) at which to insert the circuit.
 Returns
None
 append_circuit(self, circuit)¶
Append a circuit to the end of this circuit.
This circuit must satisfy the requirements of :method:`insert_circuit()`. See that method for more details.
 Parameters
circuit (A Circuit object) – The circuit to be appended.
 Returns
Circuit
 append_circuit_inplace(self, circuit)¶
Append a circuit to the end of this circuit.
This circuit must satisfy the requirements of :method:`insert_circuit()`. See that method for more details.
 Parameters
circuit (A Circuit object) – The circuit to be appended.
 Returns
None
 prefix_circuit(self, circuit)¶
Prefix a circuit to the beginning of this circuit.
This circuit must satisfy the requirements of the :method:`insert_circuit()`. See that method for more details.
 Parameters
circuit (A Circuit object) – The circuit to be prefixed.
 Returns
Circuit
 prefix_circuit_inplace(self, circuit)¶
Prefix a circuit to the beginning of this circuit.
This circuit must satisfy the requirements of the :method:`insert_circuit()`. See that method for more details.
 Parameters
circuit (A Circuit object) – The circuit to be prefixed.
 Returns
None
 tensor_circuit_inplace(self, circuit, line_order=None)¶
The tensor product of this circuit and circuit.
That is, it adds circuit to this circuit as new lines. The line labels of circuit must be disjoint from the line labels of this circuit, as otherwise applying the circuits in parallel does not make sense.
 Parameters
circuit (A Circuit object) – The circuit to be tensored.
line_order (List, optional) – A list of all the line labels specifying the order of the circuit in the updated circuit. If None, the lines of circuit are added below the lines of this circuit. Note that, for many purposes, the ordering of lines of the circuit is irrelevant.
 Returns
None
 tensor_circuit(self, circuit, line_order=None)¶
The tensor product of this circuit and circuit.
That is, it adds circuit to this circuit as new lines. The line labels of circuit must be disjoint from the line labels of this circuit, as otherwise applying the circuits in parallel does not make sense.
 Parameters
circuit (A Circuit object) – The circuit to be tensored.
line_order (List, optional) – A list of all the line labels specifying the order of the circuit in the updated circuit. If None, the lines of circuit are added below the lines of this circuit. Note that, for many purposes, the ordering of lines of the circuit is irrelevant.
 Returns
Circuit
 replace_layer_with_circuit_inplace(self, circuit, j)¶
Replaces the jth layer of this circuit with circuit.
 Parameters
circuit (Circuit) – The circuit to insert
j (int) – The layer index to replace.
 Returns
None
 replace_layer_with_circuit(self, circuit, j)¶
Replaces the jth layer of this circuit with circuit.
 Parameters
circuit (Circuit) – The circuit to insert
j (int) – The layer index to replace.
 Returns
Circuit
 replace_gatename_inplace(self, old_gatename, new_gatename)¶
Changes the name of a gate throughout this Circuit.
Note that the name is only a part of the label identifying each gate, and doesn’t include the lines (qubits) a gate acts upon. For example, the “Gx:0” and “Gx:1” labels both have the same name but act on different qubits.
 Parameters
old_gatename (str) – The gate name to replace.
new_gatename (str) – The name to replace old_gatename with.
 Returns
None
 replace_gatename(self, old_gatename, new_gatename)¶
Returns a copy of this Circuit except that old_gatename is changed to new_gatename.
Note that the “name” is only a part of the “label” identifying each gate, and doesn’t include the lines (qubits) a gate acts upon. For example, the “Gx:0” and “Gx:1” labels both have the same name but act on different qubits.
 Parameters
old_gatename (str) – The gate name to replace.
new_gatename (str) – The name to replace old_gatename with.
 Returns
Circuit
 replace_gatename_with_idle_inplace(self, gatename)¶
Treats a given gatename as an idle gate throughout this Circuit.
This effectively removes this gate name from the circuit, and replaces a layer containing only this gate name with an idle layer.
 Parameters
gatename (str) – The gate name to replace.
 Returns
None
 replace_gatename_with_idle(self, gatename)¶
Returns a copy of this Circuit with a given gatename treated as an idle gate.
This effectively removes this gate name from the circuit, and replaces a layer containing only this gate name with an idle layer.
 Parameters
gatename (str) – The gate name to replace.
 Returns
Circuit
 replace_layer(self, old_layer, new_layer)¶
Returns a copy of this Circuit except that old_layer is changed to new_layer.
 replace_layers_with_aliases(self, alias_dict)¶
Performs a find and replace using layer aliases.
Returns a copy of this Circuit except that it’s layers that match keys of alias_dict are replaced with the corresponding values.
 Parameters
alias_dict (dict) – A dictionary whose keys are layer Labels (or equivalent tuples or strings), and whose values are Circuits.
 Returns
Circuit
 change_gate_library(self, compilation, allowed_filter=None, allow_unchanged_gates=False, depth_compression=True, one_q_gate_relations=None)¶
Reexpress a circuit over a different model.
 Parameters
compilation (dict or CompilationLibrary.) –
If a dictionary, the keys are some or all of the gates that appear in the circuit, and the values are replacement circuits that are normally compilations for each of these gates (if they are not, the action of the circuit will be changed). The circuits need not be on all of the qubits, and need only satisfy the requirements of the insert_circuit method. There must be a key for every gate except the self.identity gate, unless allow_unchanged_gates is False. In that case, gate that aren’t a key in this dictionary are left unchanged.
If a CompilationLibrary, this will be queried via the retrieve_compilation_of() method to find compilations for all of the gates in the circuit. So this CompilationLibrary must contain or be able to autogenerate compilations for the requested gates, except when allow_unchanged_gates is True. In that case, gates that a compilation is not returned for are left unchanged.
allowed_filter (dict or set, optional) – Specifies which gates are allowed to be used when generating compilations from compilation. Can only be not None if compilation is a CompilationLibrary. If a dict, keys must be gate names (like “Gcnot”) and values
QubitGraph
objects indicating where that gate (if it’s present in the library) may be used. If a set, then it specifies a set of qubits and any gate in the current library that is confined within that set is allowed. If None, then all gates within the library are allowed.allow_unchanged_gates (bool, optional) – Whether to allow some gates to remain unchanged, and therefore to be absent from compilation. When True such gates are left alone; when False an error is raised if any such gates are encountered.
depth_compression (bool, optional) – Whether to perform depth compression after changing the gate library. If one_q_gate_relations is None this will only remove idle layers and compress the circuit by moving everything as far forward as is possible without knowledge of the action of any gates other than self.identity. See the depth_compression method for more details. Under most circumstances this should be true; if it is False changing gate library will often result in a massive increase in circuit depth.
one_q_gate_relations (dict, optional) – Gate relations for the onequbit gates in the new gate library, that are used in the depth compression, to cancel / combine gates. E.g., one keyvalue pair might be (‘Gh’,’Gh’) : ‘I’, to signify that two Hadamards c ompose to the idle gate ‘Gi’. See the depth_compression() method for more details.
 Returns
None
 map_names_inplace(self, mapper)¶
The names of all of the simple labels are updated inplace according to the mapping function mapper.
 Parameters
mapper (dict or function) – A dictionary whose keys are the existing gate name values and whose values are the new names (strings) or a function which takes a single (existing name) argument and returns a new name.
 Returns
None
 map_state_space_labels_inplace(self, mapper)¶
The labels of all of the lines (wires/qubits) are updated according to the mapping function mapper.
 Parameters
mapper (dict or function) – A dictionary whose keys are the existing self.line_labels values and whose value are the new labels, or a function which takes a single (existing linelabel) argument and returns a new linelabel.
 Returns
None
 map_state_space_labels(self, mapper)¶
Creates a new Circuit whose line labels are updated according to the mapping function mapper.
 Parameters
mapper (dict or function) – A dictionary whose keys are the existing self.line_labels values and whose value are the new labels, or a function which takes a single (existing linelabel) argument and returns a new linelabel.
 Returns
Circuit
 reorder_lines_inplace(self, order)¶
Reorders the lines (wires/qubits) of the circuit.
Note that the ordering of the lines is unimportant for most purposes.
 Parameters
order (list) – A list containing all of the circuit line labels (self.line_labels) in the order that the should be converted to.
 Returns
None
 reorder_lines(self, order)¶
Reorders the lines (wires/qubits) of the circuit.
Note that the ordering of the lines is unimportant for most purposes.
 Parameters
order (list) – A list containing all of the circuit line labels (self.line_labels) in the order that the should be converted to.
 Returns
Circuit
 _is_line_idling(self, line_label, idle_layer_labels=None)¶
Whether the line in question is idling in every circuit layer.
 Parameters
line_label (str or int) – The label of the line (i.e., “wire” or qubit).
idle_layer_labels (iterable, optional) – A list or tuple of layerlabels that should be treated as idle operations, so their presence will not disqualify a line from being “idle”. E.g. [“Gi”] will cause “Gi” layers to be considered idle layers.
 Returns
bool – True if the line is idling. False otherwise.
 idling_lines(self, idle_layer_labels=None)¶
Returns the line labels corresponding to idling lines.
 Parameters
idle_layer_labels (iterable, optional) – A list or tuple of layerlabels that should be treated as idle operations, so their presence will not disqualify a line from being “idle”. E.g. [“Gi”] will cause “Gi” layers to be considered idle layers.
 Returns
tuple
 delete_idling_lines_inplace(self, idle_layer_labels=None)¶
Removes from this circuit all lines that are idling at every layer.
 Parameters
idle_layer_labels (iterable, optional) – A list or tuple of layerlabels that should be treated as idle operations, so their presence will not disqualify a line from being “idle”. E.g. [“Gi”] will cause “Gi” layers to be considered idle layers.
 Returns
None
 delete_idling_lines(self, idle_layer_labels=None)¶
Removes from this circuit all lines that are idling at every layer.
 Parameters
idle_layer_labels (iterable, optional) – A list or tuple of layerlabels that should be treated as idle operations, so their presence will not disqualify a line from being “idle”. E.g. [“Gi”] will cause “Gi” layers to be considered idle layers.
 Returns
Circuit
 replace_with_idling_line_inplace(self, line_label, clear_straddlers=True)¶
Converts the specified line to an idling line, by removing all its gates.
If there are any multiqubit gates acting on this line, this function will raise an error when clear_straddlers=False.
 Parameters
line_label (str or int) – The label of the line to convert to an idling line.
clear_straddlers (bool, optional) – Whether or not gates which straddle the line_label should also be cleared. If False and straddling gates exist, an error will be raised.
 Returns
None
 reverse_inplace(self)¶
Reverses the order of the circuit.
 Returns
None
 _combine_one_q_gates_inplace(self, one_q_gate_relations)¶
Compresses sequences of 1qubit gates in the circuit, using the provided gate relations.
One of the steps of the depth_compression() method, and in most cases that method will be more useful.
 Parameters
one_q_gate_relations (dict) –
Keys that are pairs of strings, corresponding to 1qubit gate names, with values that are a single string, also corresponding to a 1qubit gate name. Whenever a 1qubit gate with name name1 is followed in the circuit by a 1qubit gate with name2 then, if one_q_gate_relations[name1,name2] = name3, name1 > name3 and name2 > self.identity, the identity name in the circuit. Moreover, this is still implemented when there are self.identity gates between these 1qubit gates, and it is implemented iteratively in the sense that if there is a sequence of 1qubit gates with names name1, name2, name3, … and there are relations for all of (name1,name2) > name12, (name12,name3) > name123 etc then the entire sequence of 1qubit gates will be compressed into a single possibly nonidle 1qubit gate followed by idle gates in place of the previous 1qubit gates. Note that None can be used as name3 to signify that the result is the identity (no gate labels).
If a QubitProcessorSpec object has been created for the gates/device in question, the QubitProcessorSpec.oneQgate_relations is the appropriate (and autogenerated) one_q_gate_relations.
Note that this function will not compress sequences of 1qubit gates that cannot be compressed by independently inspecting sequential nonidle pairs (as would be the case with, for example, Gxpi Gzpi Gxpi Gzpi, if the relation did not know that (Gxpi,Gzpi) > Gypi, even though the sequence is the identity).
 Returns
bool – False if the circuit is unchanged, and True otherwise.
 _shift_gates_forward_inplace(self)¶
Shift all gates forward (left) as far as is possible.
This operation is performed without any knowledge of what any of the gates are. One of the steps of :method:`depth_compression()`.
 Returns
bool – False if the circuit is unchanged, and True otherwise.
 delete_idle_layers_inplace(self)¶
Deletes all layers in this circuit that contain no gate operations.
One of the steps of the depth_compression() method.
 Returns
bool – False if the circuit is unchanged, and True otherwise.
 compress_depth_inplace(self, one_q_gate_relations=None, verbosity=0)¶
Compresses the depth of this circuit using very simple rewrite rules.
If one_q_gate_relations is provided, all sequences of 1qubit gates in the circuit are compressed as far as is possible using only the pairwise combination rules provided by this dict (see below).
All gates are shifted forwarded as far as is possible without any knowledge of what any of the gates are.
All idleonly layers are deleted.
 Parameters
one_q_gate_relations (dict) –
Keys that are pairs of strings, corresponding to 1qubit gate names, with values that are a single string, also corresponding to a 1qubit gate name. Whenever a 1qubit gate with name name1 is followed in the circuit by a 1qubit gate with name2 then, if one_q_gate_relations[name1,name2] = name3, name1 > name3 and name2 > self.identity, the identity name in the circuit. Moreover, this is still implemented when there are self.identity gates between these 1qubit gates, and it is implemented iteratively in the sense that if there is a sequence of 1qubit gates with names name1, name2, name3, … and there are relations for all of (name1,name2) > name12, (name12,name3) > name123 etc then the entire sequence of 1qubit gates will be compressed into a single possibly nonidle 1qubit gate followed by idle gates in place of the previous 1qubit gates.
If a QubitProcessorSpec object has been created for the gates/device in question, the QubitProcessorSpec.oneQgate_relations is the appropriate (and autogenerated) one_q_gate_relations.
Note that this function will not compress sequences of 1qubit gates that cannot be compressed by independently inspecting sequential nonidle pairs (as would be the case with, for example, Gxpi Gzpi Gxpi Gzpi, if the relation did not know that (Gxpi,Gzpi) > Gypi, even though the sequence is the identity).
verbosity (int, optional) – If > 0, information about the depth compression is printed to screen.
 Returns
None
 layer(self, j)¶
Returns a tuple of the components, i.e. the (nonidentity) gates, in the layer at depth j.
These are the .components of the
Label
returned by indexing this Circuit (using square brackets) with j, i.e. this returns this_circuit[j].components. Parameters
j (int) – The index (depth) of the layer to be returned
 Returns
tuple
 layer_label(self, j)¶
Returns the layer, as a
Label
, at depth j.This label contains as components all the (nonidentity) gates in the layer..
 Parameters
j (int) – The index (depth) of the layer to be returned
 Returns
Label
 layer_with_idles(self, j, idle_gate_name='I')¶
Returns a tuple of the components of the layer at depth j, with idle_gate_name at empty circuit locations.
This effectively places an explicit idle_gate_name gates wherever there is an implied identity operation in the circuit.
 Parameters
j (int) – The index (depth) of the layer to be returned
idle_gate_name (str, optional) – The idle gate name to use. Note that state space (qubit) labels will be added to this name to form a
Label
.
 Returns
tuple
 layer_label_with_idles(self, j, idle_gate_name='I')¶
Returns the layer, as a
Label
, at depth j, with idle_gate_name at empty circuit locations.This effectively places an explicit idle_gate_name gates wherever there is an implied identity operation in the circuit.
 Parameters
j (int) – The index (depth) of the layer to be returned
idle_gate_name (str, optional) – The idle gate name to use. Note that state space (qubit) labels will be added to this name to form a
Label
.
 Returns
Label
 property num_layers(self)¶
The number of circuit layers.
In simple circuits, this is the same as the depth (given by :method:`depth`). For circuits containing subcircuit blocks, this gives the number of toplevel layers in this circuit.
 Returns
int
 property depth(self)¶
The circuit depth.
This is the number of layers in simple circuits. For circuits containing subcircuit blocks, this includes the full depth of these blocks. If you just want the number of toplevel layers, use :method:`num_layers`.
 Returns
int
 property width(self)¶
The circuit width.
This is the number of qubits on which the circuit acts. This includes qubits that only idle, but are included as part of the circuit according to self.line_labels.
 Returns
int
 property size(self)¶
Returns the circuit size.
This is the sum of the sizes of all the gates in the circuit. A gate that acts on nqubits has a size of n, with the exception of the idle which has a size of 0. Hence, the circuit is given by: size = depth * num_lines  num_1Q_idles.
 Returns
int
 property duration(self)¶
 two_q_gate_count(self)¶
The number of twoqubit gates in the circuit.
(Note that this cannot distinguish between “true” 2qubit gates and gate that have been defined to act on two qubits but that represent some tensorproduct gate.)
 Returns
int
 num_nq_gates(self, nq)¶
The number of nqqubit gates in the circuit.
(Note that this cannot distinguish between “true” nqqubit gates and gate that have been defined to act on nq qubits but that represent some tensorproduct gate.)
 Parameters
nq (int) – The qubitcount of the gates to count. For example, if nq == 3, this function returns the number of 3qubit gates.
 Returns
int
 property num_multiq_gates(self)¶
The number of multiqubit (2+ qubits) gates in the circuit.
(Note that this cannot distinguish between “true” multiqubit gates and gate that have been defined to act on more than one qubit but that represent some tensorproduct gate.)
 Returns
int
 _togrid(self, identity_name)¶
return a listoflists rep?
 __str__(self)¶
A text rendering of the circuit.
 __repr__(self)¶
Return repr(self).
 format_display_str(self, width=80)¶
Formats a string for displaying this circuit suject to a maximum width.
 Parameters
width (int, optional) – The maximum width in characters. If the circuit is longer than this width it is wrapped using multiple lines (like a musical score).
 Returns
str
 _print_labelinfo(self)¶
A useful debug routine for printing the internal label structure of a circuit
 abstract _write_q_circuit_tex(self, filename)¶
Writes a LaTeX file for rendering this circuit nicely.
Creates a file containing LaTex that will display this circuit using the Qcircuit.tex LaTex import (compiling the LaTex requires that you have the Qcircuit.tex file).
 Parameters
filename (str) – The file to write the LaTex into. Should end with ‘.tex’
 Returns
None
 convert_to_cirq(self, qubit_conversion, wait_duration=None, gatename_conversion=None, idle_gate_name='Gi')¶
Converts this circuit to a Cirq circuit.
 Parameters
qubit_conversion (dict) – Mapping from qubit labels (e.g. integers) to Cirq qubit objects.
wait_duration (cirq.Duration, optional) – If no gatename_conversion dict is given, the idle operation is not converted to a gate. If wait_diration is specified and gatename_conversion is not specified, then the idle operation will be converted to a cirq.WaitGate with the specified duration.
gatename_conversion (dict, optional) – If not None, a dictionary that converts the gatenames in the circuit to the Cirq gates that will appear in the Cirq circuit. If only standard pyGSTi names are used (e.g., ‘Gh’, ‘Gp’, ‘Gcnot’, ‘Gcphase’, etc) this dictionary need not be specified, and an automatic conversion to the standard Cirq names will be implemented.
idle_gate_name (str, optional) – Name to use for idle gates. Defaults to ‘Gi’
 Returns
A Cirq Circuit object.
 convert_to_quil(self, num_qubits=None, gatename_conversion=None, qubit_conversion=None, readout_conversion=None, block_between_layers=True, block_idles=True, gate_declarations=None)¶
Converts this circuit to a quil string.
 Parameters
num_qubits (int, optional) – The number of qubits for the quil file. If None, then this is assumed to equal the number of line labels in this circuit.
gatename_conversion (dict, optional) – A dictionary mapping gate names contained in this circuit to the corresponding gate names used in the rendered quil. If None, a standard set of conversions is used (see :function:`standard_gatenames_quil_conversions`).
qubit_conversion (dict, optional) – If not None, a dictionary converting the qubit labels in the circuit to the desired qubit labels in the quil output. Can be left as None if the qubit labels are either (1) integers, or (2) of the form ‘Qi’ for integer i. In this case they are converted to integers (i.e., for (1) the mapping is trivial, for (2) the mapping strips the ‘Q’).
readout_conversion (dict, optional) – If not None, a dictionary converting the qubit labels mapped through qubit_conversion to the bit labels for readot. E.g. Suppose only qubit 2 (on Rigetti hardware) is in use. Then the pyGSTi string will have only one qubit (labeled 0); it will get remapped to 2 via qubit_conversion={0:2}. At the end of the quil circuit, readout should go recorded in bit 0, so readout_conversion = {0:0}. (That is, qubit with pyGSTi label 0 gets read to Rigetti bit 0, even though that qubit has Rigetti label 2.)
block_between_layers (bool, optional) – When True, add in a barrier after every circuit layer. Including such “pragma” blocks can be important for QCVV testing, as this can help reduce the “behindthescenes” compilation (beyond necessary conversion to native instructions) experience by the circuit.
block_idles (bool, optional) – In the special case of global idle gates, pragmablock barriers are inserted even when block_between_layers=False. Set block_idles=False to disable this behavior, whcih typically results in global idle gates being removed by the compiler.
gate_declarations (dict, optional) – If not None, a dictionary that provides unitary maps for particular gates that are not already in the quil syntax.
 Returns
str – A quil string.
 convert_to_openqasm(self, num_qubits=None, standard_gates_version='u3', gatename_conversion=None, qubit_conversion=None, block_between_layers=True, block_between_gates=False, gateargs_map=None)¶
Converts this circuit to an openqasm string.
 Parameters
num_qubits (int, optional) – The number of qubits for the openqasm file. If None, then this is assumed to equal the number of line labels in this circuit.
version (string, optional) – Either ‘u3’ or ‘xsxrz’. Specifies the naming convention for the QASM gates. With ‘u3’, all singlequbit gates are specified in terms of the ‘u3’ gate, used by IBM and QisKit until ~2021 (see the qasm_u3 function). With ‘xsxrz’, all singlegates are specified in terms of ‘x’ (an x pi rotation), ‘sx’ (an x pi/2 rotation) and ‘rz’ (a parameterized rotation around z by an angle theta).
gatename_conversion (dict, optional) – If not None, a dictionary that converts the gatenames in the circuit to the gatenames that will appear in the openqasm output. If only standard pyGSTi names are used (e.g., ‘Gh’, ‘Gp’, ‘Gcnot’, ‘Gcphase’, etc) this dictionary need not be specified, and an automatic conversion to the standard openqasm names will be implemented.
qubit_conversion (dict, optional) – If not None, a dictionary converting the qubit labels in the circuit to the desired qubit labels in the openqasm output. Can be left as None if the qubit labels are either (1) integers, or (2) of the form ‘Qi’ for integer i. In this case they are converted to integers (i.e., for (1) the mapping is trivial, for (2) the mapping strips the ‘Q’).
block_between_layers (bool, optional) – When True, add in a barrier after every circuit layer. Including such barriers can be important for QCVV testing, as this can help reduce the “behindthescenes” compilation (beyond necessary conversion to native instructions) experience by the circuit.
gateargs_map (dict, optional) – If not None, a dict that maps strings (representing pyGSTi standard gate names) to functions that map the parameters of a pyGSTi gate to a string to be combined with the QASM name to specify the specific gate, in QASM. If only standard pyGSTi names are used (e.g., ‘Gh’, ‘Gzr’, ‘Gczr, etc) or none of the gates are parameterized, this dictionary need not be specified, and an automatic conversion to the standard openqasm format will be implemented.
 Returns
str – An openqasm string.
 simulate(self, model, return_all_outcomes=False)¶
Compute the outcome probabilities of this Circuit using model as a model for the gates.
The order of the outcome strings (e.g., ‘0100’) is w.r.t. to the ordering of the qubits in the circuit. That is, the ith element of the outcome string corresponds to the qubit with label self.line_labels[i].
 Parameters
model (Model) – A description of the gate and SPAM operations corresponding to the labels stored in this Circuit. If this model is over more qubits than the circuit, the output will be the probabilities for the qubits in the circuit marginalized, if possible over the other qubits. But, the simulation is over the full set of qubits in the model, and so the time taken for the simulation scales with the number of qubits in the model. For models where “spectator” qubits do not affect the qubits in this circuit (such as with perfect gates), more efficient simulations will be obtained by first creating a model only over the qubits in this circuit.
return_all_outcomes (bool, optional) – Whether to include outcomes in the returned dictionary that have zero probability. When False, the threshold for discarding an outcome as z ero probability is 10^12.
 Returns
probs (dictionary) – A dictionary with keys equal to all (return_all_outcomes is True) or possibly only some (return_all_outcomes is False) of the possible outcomes, and values that are float probabilities.
 done_editing(self)¶
Make this circuit readonly, so that it can be hashed (e.g. used as a dictionary key).
This is done automatically when attempting to hash a
Circuit
for the first time, so there’s calling this function can usually be skipped (but it’s good for code clarity). Returns
None
 expand_instruments_and_separate_povm(self, model, observed_outcomes=None)¶
Creates a dictionary of
SeparatePOVMCircuit
objects from expanding the instruments of this circuit.Each key of the returned dictionary replaces the instruments in this circuit with a selection of their members. (The size of the resulting dictionary is the product of the sizes of each instrument appearing in this circuit when observed_outcomes is None). Keys are stored as
SeparatePOVMCircuit
objects so it’s easy to keep track of which POVM outcomes (effects) correspond to observed data. This function is, for the most part, used internally to process a circuit before computing its outcome probabilities. Parameters
model (Model) –
The model used to provide necessary details regarding the expansion, including:
default SPAM layers
definitions of instrumentcontaining layers
expansions of individual instruments and POVMs
 Returns
OrderedDict – A dict whose keys are
SeparatePOVMCircuit
objects and whose values are tuples of the outcome labels corresponding to this circuit, one per POVM effect held in the key.
 class pygsti.report._CircuitList(circuits, op_label_aliases=None, circuit_rules=None, circuit_weights=None, name=None)¶
Bases:
pygsti.baseobjs.nicelyserializable.NicelySerializable
A unmutable list (a tuple) of
Circuit
objects and associated metadata. Parameters
circuits (list) – The list of circuits that constitutes the primary data held by this object.
op_label_aliases (dict, optional) – Dictionary of circuit metadata whose keys are operation label “aliases” and whose values are circuits corresponding to what that operation label should be expanded into before querying the dataset. Defaults to the empty dictionary (no aliases defined). e.g. op_label_aliases[‘Gx^3’] = pygsti.obj.Circuit([‘Gx’,’Gx’,’Gx’])
circuit_weights (numpy.ndarray, optional) – If not None, an array of percircuit weights (of length equal to the number of circuits) that are typically used to multiply the counts extracted for each circuit.
name (str, optional) – An optional name for this list, used for status messages.
 classmethod cast(cls, circuits)¶
Convert (if needed) an object into a
CircuitList
. Parameters
circuits (list or CircuitList) – The object to convert.
 Returns
CircuitList
 _to_nice_serialization(self)¶
 classmethod _from_nice_serialization(cls, state)¶
 __len__(self)¶
 __getitem__(self, index)¶
 __iter__(self)¶
 apply_aliases(self)¶
Applies any operationlabel aliases to this circuit list.
 Returns
list – A list of :class:`Circuit`s.
 truncate(self, circuits_to_keep)¶
Builds a new circuit list containing only a given subset.
This can be safer then just creating a new
CircuitList
because it preserves the aliases, etc., of this list. Parameters
circuits_to_keep (list or set) – The circuits to retain in the returned circuit list.
 Returns
CircuitList
 truncate_to_dataset(self, dataset)¶
Builds a new circuit list containing only those elements in dataset.
 Parameters
dataset (DataSet) – The dataset to check. Aliases are applied to the circuits in this circuit list before they are tested.
 Returns
CircuitList
 __hash__(self)¶
Return hash(self).
 __eq__(self, other)¶
Return self==value.
 __setstate__(self, state_dict)¶
 class pygsti.report._PlaquetteGridCircuitStructure(plaquettes, x_values, y_values, xlabel, ylabel, additional_circuits=None, op_label_aliases=None, circuit_rules=None, circuit_weights_dict=None, additional_circuits_location='start', name=None)¶
Bases:
pygsti.circuits.circuitlist.CircuitList
Encapsulates a set of circuits, along with an associated structure.
By “structure”, we mean the ability to index the circuits by a 4tuple (x, y, minor_x, minor_y) for displaying in nested color box plots, along with any aliases.
 classmethod cast(cls, circuits_or_structure)¶
Convert (if needed) an object into a circuit structure.
 Parameters
circuits_or_structure (list or CircuitList) – The object to convert. If a
PlaquetteGridCircuitStructure
, then the object is simply returned. Lists of circuits (including :class:`CircuitList`s are converted to structures having no plaquettes. Returns
PlaquetteGridCircuitStructure
 _to_nice_serialization(self)¶
 classmethod _from_nice_serialization(cls, state)¶
 property plaquettes(self)¶
 iter_plaquettes(self)¶
 plaquette(self, x, y, empty_if_missing=False)¶
The plaquette at (x,y).
 Parameters
x (various) – xvalue (not index)
y (various) – yvalue (not index)
empty_if_missing (bool, optional) – Whether an empty (0element) plaquette should be returned when the requested (x,y) is missing.
 Returns
CircuitPlaquette
 property used_xs(self)¶
The xvalues which have at least one nonempty plaquette
 Returns
list
 property used_ys(self)¶
The yvalues which have at least one nonempty plaquette
 Returns
list
 truncate(self, circuits_to_keep=None, xs_to_keep=None, ys_to_keep=None, keep_rows_cols=True)¶
Truncate this circuit structure to a subset of its current circuits.
 Parameters
circuits_to_keep (list) – Keep only the circuits present in this list (of Circuit objects).
xs_to_keep (list, optional) – The xvalues to keep. If None, then all are kept.
ys_to_keep (list, optional) – The yvalues to keep. If None, then all are kept.
keep_rows_cols (bool) – Whether to retain the same number of rows as columns (even if entire rows and/or columns are empty). By default, this is True because we usually want all the plaquettes of a
PlaquetteGridCircuitStructure
to have the same number of rows and columns.
 Returns
PlaquetteGridCircuitStructure
 nested_truncations(self, axis='x', keep_rows_cols=False)¶
Get the nested truncations of this circuit structure along an axis.
When axis == ‘x’, a list of truncations (of this structure) that keep an incrementally larger set of all the xvalues. E.g., if the xvalues are [1,2,4], truncations to [1], [1,2], and [1,2,4] (no truncation) would be returned.
Setting axis ==’y’ gives the same behavior except using the yvalues.
 Parameters
axis ({'x', 'y'}) – Which axis to truncate along (see above).
keep_rows_cols (bool) – Whether to retain the same number of rows as columns (even if entire rows and/or columns are empty).
 Returns
list – A list of
PlaquetteGridCircuitStructure
objects (truncations of this object).
 process_circuits(self, processor_fn, updated_aliases=None)¶
Create a new plaquette with circuits manipulated according to processor_fn.
 Parameters
processor_fn (function) – A function which takes a single Circuit argument and returns another (or the same) Circuit.
updated_aliases (dict, optional) – Because the Label keys of an alias dictionary (maps Label > Circuit) cannot be processed as a Circuit, one must supply a manualy processed alias dictionary. If you don’t use alias dictionaries just leave this set to None.
 Returns
PlaquetteGridCircuitStructure
 copy(self)¶
Returns a copy of this circuit structure.
 Returns
PlaquetteGridCircuitStructure
 class pygsti.report._Lbl¶
Bases:
object
A label used to identify a gate, circuit layer, or (sub)circuit.
A label consisting of a string along with a tuple of integers or sectornames specifying which qubits, or more generally, parts of the Hilbert space that is acted upon by an object solabeled.
 property depth(self)¶
The depth of this label, viewed as a subcircuit.
 property reps(self)¶
Number of repetitions (of this label’s components) that this label represents.
 property has_nontrivial_components(self)¶
 collect_args(self)¶
 strip_args(self)¶
 expand_subcircuits(self)¶
Expand any subcircuits within this label.
Returns a list of component labels which doesn’t include any
CircuitLabel
labels. This effectively expands any “boxes” or “exponentiation” within this label. Returns
tuple – A tuple of component Labels (none of which should be :class:`CircuitLabel`s).
 class pygsti.report._VerbosityPrinter(verbosity=1, filename=None, comm=None, warnings=True, split=False, clear_file=True)¶
Bases:
object
Class responsible for logging things to stdout or a file.
Controls verbosity and can print progress bars. ex:
>>> VerbosityPrinter(1)
would construct a printer that printed out messages of level one or higher to the screen.
>>> VerbosityPrinter(3, 'output.txt')
would construct a printer that sends verbose output to a text file
The static function
create_printer()
will construct a printer from either an integer or an already existing printer. it is a static method of the VerbosityPrinter class, so it is called like so:>>> VerbosityPrinter.create_printer(2)
or
>>> VerbostityPrinter.create_printer(VerbosityPrinter(3, 'output.txt'))
printer.log('status')
would log ‘status’ if the printers verbosity was one or higher.printer.log('status2', 2)
would log ‘status2’ if the printer’s verbosity was two or higherprinter.error('something terrible happened')
would ALWAYS log ‘something terrible happened’.printer.warning('something worrisome happened')
would log if verbosity was one or higher  the same as a normal status.Both printer.error and printer.warning will prepend ‘ERROR: ‘ or ‘WARNING: ‘ to the message they are given. Optionally, printer.log() can also prepend ‘Status_n’ to the message, where n is the message level.
Logging of progress bars/iterations:
>>> with printer_instance.progress_logging(verbosity): >>> for i, item in enumerate(data): >>> printer.show_progress(i, len(data)) >>> printer.log(...)
will output either a progress bar or iteration statuses depending on the printer’s verbosity
 Parameters
verbosity (int) – How verbose the printer should be.
filename (str, optional) – Where to put output (If none, output goes to screen)
comm (mpi4py.MPI.Comm or ResourceAllocation, optional) – Restricts output if the program is running in parallel (By default, if the rank is 0, output is sent to screen, and otherwise sent to commfiles 1, 2, …
warnings (bool, optional) – Whether or not to print warnings
split (bool, optional) – Whether to split output between stdout and stderr as appropriate, or to combine the streams so everything is sent to stdout.
clear_file (bool, optional) – Whether or not filename should be cleared (overwritten) or simply appended to.
 _comm_path¶
relative path where comm files (outputs of nonroot ranks) are stored.
 Type
str
 _comm_file_name¶
root filename for comm files (outputs of nonroot ranks).
 Type
str
 _comm_file_ext¶
filename extension for comm files (outputs of nonroot ranks).
 Type
str
 _comm_path =¶
 _comm_file_name =¶
 _comm_file_ext = .txt¶
 _create_file(self, filename)¶
 _get_comm_file(self, comm_id)¶
 clone(self)¶
Instead of deepcopy, initialize a new printer object and feed it some select deepcopied members
 Returns
VerbosityPrinter
 static create_printer(verbosity, comm=None)¶
Function for converting between interfaces
 Parameters
verbosity (int or VerbosityPrinter object, required:) – object to build a printer from
comm (mpi4py.MPI.Comm object, optional) – Comm object to build printers with. !Will override!
 Returns
VerbosityPrinter – The printer object, constructed from either an integer or another printer
 __add__(self, other)¶
Increase the verbosity of a VerbosityPrinter
 __sub__(self, other)¶
Decrease the verbosity of a VerbosityPrinter
 __getstate__(self)¶
 __setstate__(self, state_dict)¶
 _append_to(self, filename, message)¶
 _put(self, message, flush=True, stderr=False)¶
 _record(self, typ, level, message)¶
 error(self, message)¶
Log an error to the screen/file
 Parameters
message (str) – the error message
 Returns
None
 warning(self, message)¶
Log a warning to the screen/file if verbosity > 1
 Parameters
message (str) – the warning message
 Returns
None
 log(self, message, message_level=None, indent_char=' ', show_statustype=False, do_indent=True, indent_offset=0, end='\n', flush=True)¶
Log a status message to screen/file.
Determines whether the message should be printed based on current verbosity setting, then sends the message to the appropriate output
 Parameters
message (str) – the message to print (or log)
message_level (int, optional) – the minimum verbosity level at which this level is printed.
indent_char (str, optional) – what constitutes an “indent” (messages at higher levels are indented more when do_indent=True).
show_statustype (bool, optional) – if True, prepend lines with “Status Level X” indicating the message_level.
do_indent (bool, optional) – whether messages at higher message levels should be indented. Note that if this is False it may be helpful to set show_statustype=True.
indent_offset (int, optional) – an additional number of indentations to add, on top of any due to the message level.
end (str, optional) – the character (or string) to end message lines with.
flush (bool, optional) – whether stdout should be flushed right after this message is printed (this avoids delays in onscreen output due to buffering).
 Returns
None
 _progress_bar(self, iteration, total, bar_length, num_decimals, fill_char, empty_char, prefix, suffix, indent)¶
 _verbose_iteration(self, iteration, total, prefix, suffix, verbose_messages, indent, end)¶
 __str__(self)¶
Return str(self).
 verbosity_env(self, level)¶
Create a temporary environment with a different verbosity level.
This is context manager, controlled using Python’s with statement:
>>> with printer.verbosity_env(2): printer.log('Message1') # printed at verbosity level 2 printer.log('Message2') # printed at verbosity level 2
 Parameters
level (int) – the verbosity level of the environment.
 progress_logging(self, message_level=1)¶
Context manager for logging progress bars/iterations.
(The printer will return to its normal, unrestricted state when the progress logging has finished)
 Parameters
message_level (int, optional) – progress messages will not be shown until the verbosity level reaches message_level.
 show_progress(self, iteration, total, bar_length=50, num_decimals=2, fill_char='#', empty_char='', prefix='Progress:', suffix='', verbose_messages=[], indent_char=' ', end='\n')¶
Displays a progress message (to be used within a progress_logging block).
 Parameters
iteration (int) – the 0based current iteration – the interation number this message is for.
total (int) – the total number of iterations expected.
bar_length (int, optional) – the length, in characters, of a textformat progress bar (only used when the verbosity level is exactly equal to the progress_logging message level.
num_decimals (int, optional) – number of places after the decimal point that are displayed in progress bar’s percentage complete.
fill_char (str, optional) – replaces ‘#’ as the barfilling character
empty_char (str, optional) – replaces ‘’ as the emptybar character
prefix (str, optional) – message in front of the bar
suffix (str, optional) – message after the bar
verbose_messages (list, optional) – A list of strings to display after an initial “Iter X of Y” line when the verbosity level is higher than the progress_logging message level and so more verbose messages are shown (and a progress bar is not). The elements of verbose_messages will occur, one per line, after the initial “Iter X of Y” line.
indent_char (str, optional) – what constitutes an “indentation”.
end (str, optional) – the character (or string) to end message lines with.
 Returns
None
 _end_progress(self)¶
 start_recording(self)¶
Begins recording the output (to memory).
Begins recording (in memory) a list of (type, verbosityLevel, message) tuples that is returned by the next call to :method:`stop_recording`.
 Returns
None
 is_recording(self)¶
Returns whether this VerbosityPrinter is currently recording.
 Returns
bool
 stop_recording(self)¶
Stops recording and returns recorded output.
Stops a “recording” started by :method:`start_recording` and returns the list of (type, verbosityLevel, message) tuples that have been recorded since then.
 Returns
list
 pygsti.report._deprecated_fn(replacement=None)¶
Decorator for deprecating a function.
 Parameters
replacement (str, optional) – the name of the function that should replace it.
 Returns
function
 pygsti.report.ROBUST_SUFFIX_LIST = ['.robust', '.Robust', '.robust+', '.Robust+']¶
 pygsti.report.DEFAULT_BAD_FIT_THRESHOLD = 2.0¶
 pygsti.report._add_new_labels(running_lbls, current_lbls)¶
Simple routine to add currentlabels to a list of runninglabels without introducing duplicates and preserving order as best we can.
 pygsti.report._add_new_estimate_labels(running_lbls, estimates, combine_robust)¶
Like _add_new_labels but perform robustsuffix processing.
In particular, if combine_robust == True then do not add labels which have a “.robust” counterpart.
 pygsti.report._get_viewable_crf(est, est_lbl, mdl_lbl, verbosity=0)¶
 pygsti.report.create_offline_zip(output_dir='.')¶
Creates a zip file containing the a directory (“offline”) of files need to display “offline” reports.
This offline directory is often generated by reports when connected=False is specified..
For offline reports to display, the “offline” folder must be placed in the same directory as the report’s HTML file. This function can be used to easily obtain a copy of the offline folder for the purpose of sharing offline reports with other people. If you’re just creating your own offline reports using pyGSTi, the offline folder is automatically copied into it’s proper position  so you don’t need to call this function.
 Parameters
output_dir (str, optional) – The directory in which “offline.zip” should be place.
 Returns
None
 pygsti.report._set_toggles(results_dict, brevity, combine_robust)¶
 pygsti.report._create_master_switchboard(ws, results_dict, confidence_level, nmthreshold, printer, fmt, combine_robust, idt_results_dict=None, embed_figures=True)¶
Creates the “master switchboard” used by several of the reports
 pygsti.report._construct_idtresults(idt_idle_op, idt_pauli_dicts, gst_results_dict, printer)¶
Constructs a dictionary of idle tomography results, parallel to the GST results in gst_results_dict, where possible.
 pygsti.report._create_single_metric_switchboard(ws, results_dict, b_gauge_inv, dataset_labels, est_labels=None, embed_figures=True)¶
 pygsti.report.create_general_report(results, filename, title='auto', confidence_level=None, linlog_percentile=5, errgen_type='logGTi', nmthreshold=50, precision=None, comm=None, ws=None, auto_open=False, cachefile=None, brief=False, connected=False, link_to=None, resizable=True, autosize='initial', verbosity=1)¶
DEPRECATED: use pygsti.report.create_standard_report(…)
Deprecated since version v0.9.9: create_general_report will be removed in the next major release of pyGSTi. It is replaced by construct_standard_report, which returns a
Report
object.
 pygsti.report.create_standard_report(results, filename, title='auto', confidence_level=None, comm=None, ws=None, auto_open=False, link_to=None, brevity=0, advanced_options=None, verbosity=1)¶
Create a “standard” GST report, containing details about each estimate in results individually.
Either a PDF or HTML report is generated, based on whether filename ends in “.pdf” or not. In the richer HTMLmode, switches (dropdown boxes, buttons, etc.) allow the viewer to choose which estimate is displayed. The estimates in multiple
Results
objects can be viewed by providing a dictionary of Results objects as the results argument. Note that when comparing many estimates it is often more convenient to view the report generated bycreate_comparison_report()
, which is organized for this purpose.In PDFmode this interactivity is not possible and so results may contain just a single estimate. The chief advantage of this more limited mode is that is produces a highlyportable and selfcontained PDF file.
Deprecated since version v0.9.9: create_standard_report will be removed in the next major release of pyGSTi. It is replaced by construct_standard_report, which returns a
Report
object. Parameters
results (Results) – An object which represents the set of results from one or more GST estimation runs, typically obtained from running
run_long_sequence_gst()
orrun_stdpractice_gst()
, OR a dictionary of such objects, representing multiple GST runs to be compared (typically all with different data sets). The keys of this dictionary are used to label different data sets that are selectable in the report.filename (string, optional) – The output filename where the report file(s) will be saved. If None, then no output file is produced (but returned Workspace still caches all intermediate results).
title (string, optional) – The title of the report. “auto” causes a random title to be generated (which you may or may not like).
confidence_level (int, optional) – If not None, then the confidence level (between 0 and 100) used in the computation of confidence regions/intervals. If None, no confidence regions or intervals are computed.
comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors.
ws (Workspace, optional) – The workspace used as a scratch space for performing the calculations and visualizations required for this report. If you’re creating multiple reports with similar tables, plots, etc., it may boost performance to use a single Workspace for all the report generation.
auto_open (bool, optional) – If True, automatically open the report in a web browser after it has been generated.
link_to (list, optional) – If not None, a list of one or more items from the set {“tex”, “pdf”, “pkl”} indicating whether or not to create and include links to Latex, PDF, and Python pickle files, respectively. “tex” creates latex source files for tables; “pdf” renders PDFs of tables and plots ; “pkl” creates Python versions of plots (pickled python data) and tables (pickled pandas DataFrams).
brevity (int, optional) –
Amount of detail to include in the report. Larger values mean smaller “more briefr” reports, which reduce generation time, load time, and disk space consumption. In particular:
1: Plots showing persequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germlevel estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
advanced_options (dict, optional) –
A dictionary of advanced options for which the default values are usually are fine. Here are the possible keys of advanced_options:
 connectedbool, optional
Whether output HTML should assume an active internet connection. If True, then the resulting HTML file size will be reduced because it will link to web resources (e.g. CDN libraries) instead of embedding them.
 cachefilestr, optional
filename with cached workspace results
 linlogPercentilefloat, optional
Specifies the colorscale transition point for any logL or chi2 color box plots. The lower (100  linlogPercentile) percentile of the expected chi2 distribution is shown in a linear grayscale, and the top linlogPercentile is shown on a logarithmic colored scale.
 errgen_type: {“logGlogT”, “logTiG”, “logGTi”}
The type of error generator to compute. Allowed values are:
”logGlogT” : errgen = log(gate)  log(target_op)
”logTiG” : errgen = log( dot(inv(target_op), gate) )
”logGTi” : errgen = log( dot(gate, inv(target_op)) )
 nmthresholdfloat, optional
The threshold, in units of standard deviations, that triggers the usage of nonMarkovian error bars. If None, then nonMarkovian error bars are never computed.
 precisionint or dict, optional
The amount of precision to display. A dictionary with keys “polar”, “sci”, and “normal” can separately specify the precision for complex angles, numbers in scientific notation, and everything else, respectively. If an integer is given, it this same value is taken for all precision types. If None, then {‘normal’: 6, ‘polar’: 3, ‘sci’: 0} is used.
 resizablebool, optional
Whether plots and tables are made with resize handles and can be resized within the report.
 autosize{‘none’, ‘initial’, ‘continual’}
Whether tables and plots should be resized, either initially – i.e. just upon first rendering (“initial”) – or whenever the browser window is resized (“continual”).
 embed_figures: bool, optional
Whether figures should be embedded in the generated report.
 combine_robustbool, optional
Whether robust estimates should automatically be combined with their nonrobust counterpart when displayed in reports. (default is True).
 confidence_interval_brevityint, optional
Roughly specifies how many figures will have confidence intervals (when applicable). Defaults to ‘1’. Smaller values mean more tables will get confidence intervals (and reports will take longer to generate).
 idt_basis_dictstuple, optional
Tuple of (prepDict,measDict) paulibasis dictionaries, which map between 1qubit Pauli basis strings (e.g. ‘X’ or ‘Y’) and tuples of gate names (e.g. (‘Gx’,’Gx’)). If given, idle tomography will be performed on the ‘Gi’ gate and included in the report.
 idt_idle_oplabelLabel, optional
The label identifying the idle gate (for use with idle tomography).
 colorboxplot_bgcolorstr, optional
Background color for the color box plots in this report. Can be common color names, e.g. “black”, or string RGB values, e.g. “rgb(255,128,0)”.
verbosity (int, optional) – How much detail to send to stdout.
 Returns
Workspace – The workspace object used to create the report
 pygsti.report.create_nqnoise_report(results, filename, title='auto', confidence_level=None, comm=None, ws=None, auto_open=False, link_to=None, brevity=0, advanced_options=None, verbosity=1)¶
Creates a report designed to display results containing for nqubit noisy model estimates.
Such models are characterized by the fact that gates and SPAM objects may not have dense representations (or it may be very expensive to compute them) , and that these models are likely
CloudNoiseModel
objects or have similar structure.Deprecated since version v0.9.9: create_nqnoise_report will be removed in the next major release of pyGSTi. It is replaced by construct_standard_report, which returns a
Report
object. Parameters
results (Results) – An object which represents the set of results from one or more GST estimation runs, typically obtained from running
run_long_sequence_gst()
orrun_stdpractice_gst()
, OR a dictionary of such objects, representing multiple GST runs to be compared (typically all with different data sets). The keys of this dictionary are used to label different data sets that are selectable in the report.filename (string, optional) – The output filename where the report file(s) will be saved. If None, then no output file is produced (but returned Workspace still caches all intermediate results).
title (string, optional) – The title of the report. “auto” causes a random title to be generated (which you may or may not like).
confidence_level (int, optional) – If not None, then the confidence level (between 0 and 100) used in the computation of confidence regions/intervals. If None, no confidence regions or intervals are computed.
comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors.
ws (Workspace, optional) – The workspace used as a scratch space for performing the calculations and visualizations required for this report. If you’re creating multiple reports with similar tables, plots, etc., it may boost performance to use a single Workspace for all the report generation.
auto_open (bool, optional) – If True, automatically open the report in a web browser after it has been generated.
link_to (list, optional) – If not None, a list of one or more items from the set {“tex”, “pdf”, “pkl”} indicating whether or not to create and include links to Latex, PDF, and Python pickle files, respectively. “tex” creates latex source files for tables; “pdf” renders PDFs of tables and plots ; “pkl” creates Python versions of plots (pickled python data) and tables (pickled pandas DataFrams).
brevity (int, optional) –
Amount of detail to include in the report. Larger values mean smaller “more briefr” reports, which reduce generation time, load time, and disk space consumption. In particular:
1: Plots showing persequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germlevel estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
advanced_options (dict, optional) –
A dictionary of advanced options for which the default values are usually are fine. Here are the possible keys of advanced_options:
 connectedbool, optional
Whether output HTML should assume an active internet connection. If True, then the resulting HTML file size will be reduced because it will link to web resources (e.g. CDN libraries) instead of embedding them.
 cachefilestr, optional
filename with cached workspace results
 linlogPercentilefloat, optional
Specifies the colorscale transition point for any logL or chi2 color box plots. The lower (100  linlogPercentile) percentile of the expected chi2 distribution is shown in a linear grayscale, and the top linlogPercentile is shown on a logarithmic colored scale.
 nmthresholdfloat, optional
The threshold, in units of standard deviations, that triggers the usage of nonMarkovian error bars. If None, then nonMarkovian error bars are never computed.
 precisionint or dict, optional
The amount of precision to display. A dictionary with keys “polar”, “sci”, and “normal” can separately specify the precision for complex angles, numbers in scientific notation, and everything else, respectively. If an integer is given, it this same value is taken for all precision types. If None, then {‘normal’: 6, ‘polar’: 3, ‘sci’: 0} is used.
 resizablebool, optional
Whether plots and tables are made with resize handles and can be resized within the report.
 autosize{‘none’, ‘initial’, ‘continual’}
Whether tables and plots should be resized, either initially – i.e. just upon first rendering (“initial”) – or whenever the browser window is resized (“continual”).
 combine_robustbool, optional
Whether robust estimates should automatically be combined with their nonrobust counterpart when displayed in reports. (default is True).
 confidence_interval_brevityint, optional
Roughly specifies how many figures will have confidence intervals (when applicable). Defaults to ‘1’. Smaller values mean more tables will get confidence intervals (and reports will take longer to generate).
 colorboxplot_bgcolorstr, optional
Background color for the color box plots in this report. Can be common color names, e.g. “black”, or string RGB values, e.g. “rgb(255,128,0)”.
verbosity (int, optional) – How much detail to send to stdout.
 Returns
Workspace – The workspace object used to create the report
 pygsti.report.create_report_notebook(results, filename, title='auto', confidence_level=None, auto_open=False, connected=False, verbosity=0)¶
Create a “report notebook”.
A Jupyter ipython notebook file which, when its cells are executed, will generate similar figures to those contained in an html report (via
create_standard_report()
).A notebook report allows the user to interact more flexibly with the data underlying the figures, and to easily generate customized variants on the figures. As such, this type of report will be most useful for experts who want to tinker with the standard analysis presented in the static HTML or LaTeX format reports.
Deprecated since version v0.9.9: create_report_notebook will be removed in the next major release of pyGSTi. It is replaced by the Report.write_notebook
 Parameters
results (Results) – An object which represents the set of results from one or more GST estimation runs, typically obtained from running
run_long_sequence_gst()
orrun_stdpractice_gst()
, OR a dictionary of such objects, representing multiple GST runs to be compared (typically all with different data sets). The keys of this dictionary are used to label different data sets that are selectable (via setting Python variables) in the report.filename (string, optional) – The output filename where the report file(s) will be saved. Must end in “.ipynb”.
title (string, optional) – The title of the report. “auto” causes a random title to be generated (which you may or may not like).
confidence_level (int, optional) – If not None, then the confidence level (between 0 and 100) used in the computation of confidence regions/intervals. If None, no confidence regions or intervals are computed.
auto_open (bool, optional) – If True, automatically open the report in a web browser after it has been generated.
connected (bool, optional) – Whether output notebook should assume an active internet connection. If True, then the resulting file size will be reduced because it will link to web resources (e.g. CDN libraries) instead of embedding them.
verbosity (int, optional) – How much detail to send to stdout.
 Returns
None
 pygsti.report.find_std_clifford_compilation(model, verbosity=0)¶
Returns the standard Clifford compilation for model, if one exists. Otherwise returns None.
 Parameters
model (Model) – The ideal (target) model of primitive gates.
verbosity (int, optional) – How much detail to send to stdout.
 Returns
dict or None – The Clifford compilation dictionary (if one can be found).
 pygsti.report.construct_standard_report(results, title='auto', confidence_level=None, comm=None, ws=None, advanced_options=None, verbosity=1)¶
Create a “standard” GST report, containing details about each estimate in results individually.
 Parameters
results (Results) – An object which represents the set of results from one or more GST estimation runs, typically obtained from running
run_long_sequence_gst()
orrun_stdpractice_gst()
, OR a dictionary of such objects, representing multiple GST runs to be compared (typically all with different data sets). The keys of this dictionary are used to label different data sets that are selectable in the report.title (string, optional) – The title of the report. “auto” causes a random title to be generated (which you may or may not like).
confidence_level (int, optional) – If not None, then the confidence level (between 0 and 100) used in the computation of confidence regions/intervals. If None, no confidence regions or intervals are computed.
comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors.
ws (Workspace, optional) – The workspace used as a scratch space for performing the calculations and visualizations required for this report. If you’re creating multiple reports with similar tables, plots, etc., it may boost performance to use a single Workspace for all the report generation.
advanced_options (dict, optional) –
A dictionary of advanced options for which the default values are usually are fine. Here are the possible keys of advanced_options:
 linlogPercentilefloat, optional
Specifies the colorscale transition point for any logL or chi2 color box plots. The lower (100  linlogPercentile) percentile of the expected chi2 distribution is shown in a linear grayscale, and the top linlogPercentile is shown on a logarithmic colored scale.
 nmthresholdfloat, optional
The threshold, in units of standard deviations, that triggers the usage of nonMarkovian error bars. If None, then nonMarkovian error bars are never computed.
 embed_figures: bool, optional
Whether figures should be embedded in the generated report.
 combine_robustbool, optional
Whether robust estimates should automatically be combined with their nonrobust counterpart when displayed in reports. (default is True).
 idt_basis_dictstuple, optional
Tuple of (prepDict,measDict) paulibasis dictionaries, which map between 1qubit Pauli basis strings (e.g. ‘X’ or ‘Y’) and tuples of gate names (e.g. (‘Gx’,’Gx’)). If given, idle tomography will be performed on the ‘Gi’ gate and included in the report.
 idt_idle_oplabelLabel, optional
The label identifying the idle gate (for use with idle tomography).
verbosity (int, optional) – How much detail to send to stdout.
 Returns
Workspace – The workspace object used to create the report
 pygsti.report.construct_nqnoise_report(results, title='auto', confidence_level=None, comm=None, ws=None, advanced_options=None, verbosity=1)¶
Creates a report designed to display results containing for nqubit noisy model estimates.
Such models are characterized by the fact that gates and SPAM objects may not have dense representations (or it may be very expensive to compute them) , and that these models are likely
CloudNoiseModel
objects or have similar structure. Parameters
results (Results) – An object which represents the set of results from one or more GST estimation runs, typically obtained from running
run_long_sequence_gst()
orrun_stdpractice_gst()
, OR a dictionary of such objects, representing multiple GST runs to be compared (typically all with different data sets). The keys of this dictionary are used to label different data sets that are selectable in the report.title (string, optional) – The title of the report. “auto” causes a random title to be generated (which you may or may not like).
confidence_level (int, optional) – If not None, then the confidence level (between 0 and 100) used in the computation of confidence regions/intervals. If None, no confidence regions or intervals are computed.
comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors.
ws (Workspace, optional) – The workspace used as a scratch space for performing the calculations and visualizations required for this report. If you’re creating multiple reports with similar tables, plots, etc., it may boost performance to use a single Workspace for all the report generation.
advanced_options (dict, optional) –
A dictionary of advanced options for which the default values are usually are fine. Here are the possible keys of advanced_options:
 linlogPercentilefloat, optional
Specifies the colorscale transition point for any logL or chi2 color box plots. The lower (100  linlogPercentile) percentile of the expected chi2 distribution is shown in a linear grayscale, and the top linlogPercentile is shown on a logarithmic colored scale.
 nmthresholdfloat, optional
The threshold, in units of standard deviations, that triggers the usage of nonMarkovian error bars. If None, then nonMarkovian error bars are never computed.
 combine_robustbool, optional
Whether robust estimates should automatically be combined with their nonrobust counterpart when displayed in reports. (default is True).
 confidence_interval_brevityint, optional
Roughly specifies how many figures will have confidence intervals (when applicable). Defaults to ‘1’. Smaller values mean more tables will get confidence intervals (and reports will take longer to generate).
 embed_figures: bool, optional
Whether figures should be embedded in the generated report.
verbosity (int, optional) – How much detail to send to stdout.
 Returns
class:Report : A constructed report object
 pygsti.report.create_drift_report(results, title='auto', ws=None, verbosity=1)¶
Creates a Drift report.
 Parameters
results (StabilityAnalysisResults) – The driftanalysis results to create the report from.
title (string, optional) – The title of the report. “auto” causes a random title to be generated (which you may or may not like).
ws (Workspace, optional) – The workspace used as a scratch space for performing the calculations and visualizations required for this report. If you’re creating multiple reports with similar tables, plots, etc., it may boost performance to use a single Workspace for all the report generation.
verbosity (int, optional) – How much detail to send to stdout.
 Returns
Report (A constructed report object)
 pygsti.report.blues¶
 pygsti.report.empty_volumetric_plot(figsize=None, y_values=None, x_values=None, title=None, xlabel='Depth', ylabel='Width')¶
Creates an empty volumetric plot with just the axes set.
 Parameters
figsize (tuple or None, optional) – The figure size.
y_values (list or None, optional) – The yaxis values, typically corresponding to circuit widths.
x_values (list or None, optional) – The xaxis values, typically corresponding to circuit depths.
title (string or None, optional) – Plot title
xlabel (string, optional) – xaxis label
ylabel (string, optional) – yaxis label.
 Returns
fig, ax (matplolib fig and ax.)
 pygsti.report._get_xy(data, y_values=None, x_values=None)¶
 pygsti.report.volumetric_plot(data, y_values=None, x_values=None, title=None, fig=None, ax=None, cmap=my_cmap, color=None, flagQV=False, qv_threshold=None, figsize=(10, 10), scale=1.0, centerscale=1.0, linescale=1.0, pass_threshold=0, show_threshold=0)¶
Creates a volumetric benchmarking plot.
 pygsti.report.volumetric_boundary_plot(data, y_values=None, x_values=None, boundary=None, threshold=0.5, missing_data_action='continue', monotonic=True, color='k', linewidth=4, linestyle='', dashing=None, fig=None, ax=None, figsize=None, title=None, label=None)¶
Creates a volumetric benchmarking boundary plot, that displays boundary at which the given data drops below the specified threshold
 pygsti.report.capability_region_plot(vbdataframe, metric='polarization', threshold=1 / _np.e, significance=0.05, figsize=(10, 10), scale=1.0, title=None, colors=None)¶
Creates a capability regions plot from a VBDataFrame. Default options creates plots like those shown in Fig. 3 of “Measuring the Capabilities of Quantum Computers” arXiv:2008.11294.
 pygsti.report.volumetric_distribution_plot(vbdataframe, metric='polarization', threshold=1 / _np.e, hypothesis_test='standard', significance=0.05, figsize=(10, 10), scale={'min': 1.95, 'mean': 1, 'max': 0.13}, title=None, cmap=my_cmap)¶
Creates volumetric benchmarking plots that display the maximum, mean and minimum of a given figureofmerit (by default, circuit polarization) as a function of circuit shape. This function can be used to create figures like those shown in Fig. 1 of “Measuring the Capabilities of Quantum Computers” arXiv:2008.11294.
 Parameters
vbdataframe (VBDataFrame) – A VBDataFrame object containing the data to be plotted in a VB plot.
metric (string, optional) – The quantity to plot. Default is ‘polarization’ as used and defined in arXiv:2008.11294. The plot will show the maximum, mean, and minimum of this metric at each circuit shape.
threshold (float, optional) – The threshold for “success” for the figureofmerit defined by metric. This threshold is used to compute the three “success” boundaries that are shown in the plot.
hypothesis_test (string, optional) –
The type of statistical significance adjustment to apply to the boundaries. The options are  ‘standard’: this reproduces the method used and described in arXiv:2008.11294 (see the
appendices for details). With this option, there will be a difference between the boundary for the minimum and maximum polarization only if there is statistically significant evidence in the data for this.
 ’none’: no statistical significance adjustment: all three boundaries show the point at which
relevant statistic (maximum, mean, minimum) drops below the threshold.
significance (float, optional) – The statistical significance in the hypothesis tests. Only used in hypothesis_test is not ‘none’.
figsize (tuple, optional) – The figure size
scale (dict, optional) – The scale for the three concentric squares, showing the maximum, mean and minimum.
title (sting, optional) – The figure title.
cmap (ColorMap, optional) – A matplotlib colormap.
 Returns
fig, ax (matplolib fig and ax.)