pygsti.report
pyGSTi Reporting Python Package
Subpackages
Submodules
pygsti.report.autotitlepygsti.report.cellpygsti.report.colormapspygsti.report.convertpygsti.report.factorypygsti.report.figurepygsti.report.fogidiagrampygsti.report.formatterpygsti.report.formatterspygsti.report.htmlpygsti.report.latexpygsti.report.merge_helperspygsti.report.modelfunctionpygsti.report.mpl_colormapspygsti.report.notebookpygsti.report.notebookcellpygsti.report.parse_notebook_textpygsti.report.plothelperspygsti.report.plotly_plot_expygsti.report.pythonpygsti.report.reportpygsti.report.reportableqtypygsti.report.reportablespygsti.report.rowpygsti.report.tablepygsti.report.textblockpygsti.report.vbplotpygsti.report.workspacepygsti.report.workspaceplotspygsti.report.workspacetablespygsti.report.workspacetexts
Package Contents
Classes
Python representation of an IPython notebook |
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The internal model of a report. |
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Gate eigenvalues |
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Circuit eigenvalues |
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1/2 diamond norm of difference between productA(circuit) and productB(circuit) |
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Half the diamond distance bewteen model_a.operations[op_label] and model_b.operations[op_label] |
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Central to data analysis, Workspace objects facilitate the building of reports and dashboards. |
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A wildcard budget containing a single scaling parameter. |
Functions
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Evaluate a ModelFunction object using confidence region information |
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SPAM dot products (concatenates POVMS) |
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Choi matrix |
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Choi matrix eigenvalues |
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Trace of the Choi matrix |
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Eigenvalues of dot(productB(circuit)^-1, productA(circuit)) |
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Frobenius distance btwn productA(circuit) and productB(circuit) |
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Entanglement infidelity btwn productA(circuit) and productB(circuit) |
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Average gate infidelity between productA(circuit) and productB(circuit). |
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Jamiolkowski trace distance between productA(circuit) and productB(circuit) |
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Nonunitary entanglement infidelity between productA(circuit) and productB(circuit) |
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Nonunitary average gate infidelity between productA(circuit) and productB(circuit). |
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Eigenvalue entanglement infidelity between productA(circuit) and productB(circuit). |
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Eigenvalue average gate infidelity between productA(circuit) and productB(circuit). |
Eigenvalue nonunitary entanglement infidelity between productA(circuit) and productB(circuit). |
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Eigenvalue nonunitary average gate infidelity between productA(circuit) and productB(circuit). |
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Eigenvalue diamond distance between productA(circuit) and productB(circuit). |
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Eigenvalue nonunitary diamond distance between productA(circuit) and productB(circuit). |
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POVM entanglement infidelity between model_a and model_b. |
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POVM Jamiolkowski trace distance between model_a and model_b |
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Half the POVM diamond distance between model_a and model_b. |
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DEPRECATED: Decompose a 1Q gate into rotations about axes. |
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Upper bound on entanglement fidelity |
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Jamiolkowski state of closest unitary to gate |
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Fidelity between gate and its closest unitary |
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Jamiolkowski trace distance between gate and its closest unitary |
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Array of angles between the rotation axes of the gates of model. |
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Entanglement fidelity between a and b |
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Entanglement infidelity between a and b |
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Entanglement infidelity between closest unitaries to a and b |
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Frobenius distance between a and b |
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Jamiolkowski trace distance between a and b |
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a gauge-invariant quantity that behaves like the unitarity |
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a gauge-invariant quantity that behaves like the unitarity |
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Returns (d^2 - 1)/d^2 * (1 - sqrt(U)), where U is the unitarity of a*b^{-1} |
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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 eigenvalue-unitarity of a*b^{-1} |
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Returns (d - 1)/d * (1 - sqrt(U)), where U is the eigenvalue-unitarity of a*b^{-1} |
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Eigenvalue entanglement infidelity between a and b |
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Eigenvalue average gate infidelity between a and b |
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Eigenvalue diamond distance between a and b |
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Eigenvalue nonunitary diamond distance between a and b |
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Returns the average gate infidelity between a and b, where b is the "target" operation. |
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Angle between the rotation axes of a and b (1-qubit gates) |
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Eigenvalues of b^{-1} * a |
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Eigenvalues of log(b^{-1} * a) |
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Eigenvalues of log(a * b^{-1}) |
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Eigenvalues of log(a) - log(b) |
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Eigenvalues of b^{-1} * a |
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Project errgen on all of the standard sets of error generators. |
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Projections of log(b^{-1}*a). |
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Projections of log(a*b^{-1}). |
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Projections of log(a)-log(b). |
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Projections of log(A*B^{-1}) using a gauge-robust technique. |
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Decomposition of gates in model_a using those in model_b as their targets. |
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Average model infidelity |
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Prediction of RB number based on estimated (A) and target (B) models |
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State fidelity between state vectors a and b |
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State infidelity fidelity between state vectors a and b |
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Trace distance between state vectors a and b |
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State vector as a standard density matrix |
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Eigenvalues of the density matrix corresponding to a state vector |
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Returns a nice human-readable name and tooltip for a given gate-function abbreviation. |
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Evaluates that gate-function named by the abbreviation name. |
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Infidelity between instruments a and b |
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The diamond norm distance between instruments a and b. |
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Evaluates that instrument-function named by the abbreviation name. |
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Creates a zip file containing the a directory ("offline") of files need to display "offline" reports. |
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DEPRECATED: use pygsti.report.create_standard_report(...) |
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Create a "standard" GST report, containing details about each estimate in results individually. |
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Creates a report designed to display results containing for n-qubit noisy model estimates. |
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Create a "report notebook". |
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Returns the standard Clifford compilation for model, if one exists. Otherwise returns None. |
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Create a "standard" GST report, containing details about each estimate in results individually. |
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Creates a report designed to display results containing for n-qubit noisy model estimates. |
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Creates a Drift report. |
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Creates an empty volumetric plot with just the axes set. |
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Creates a volumetric benchmarking plot. |
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Creates a volumetric benchmarking boundary plot, that displays boundary at which the given data |
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Creates a capability regions plot from a VBDataFrame. Default options creates plots like those shown |
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Creates volumetric benchmarking plots that display the maximum, mean and minimum of a given figure-of-merit (by |
Attributes
- class pygsti.report.Notebook(cells=None, notebook_text_files=None)
Bases:
objectPython representation of an IPython notebook
Parameters
- cellslist, optional
List of
NotebookCellobjects.- notebook_text_fileslist, optional
List of filenames (text files with ‘@@markdown’ or ‘@@code’ designating cells).
Attributes
- DefaultTemplatestr
The default template notebook to use (a .ipynb file).
Create an IPython notebook from a list of cells, list of notebook_text_files, or both.
Parameters
- cellslist, optional
List of NotebookCell objects
- notebook_text_fileslist, optional
List of filenames (text files with ‘@@markdown’ or ‘@@code’ designating cells)
- DefaultTemplate = 'Empty.ipynb'
- to_json_dict(template_filename=DefaultTemplate)
Using an existing (usually empty) notebook as a template, generate the json for a new notebook.
Parameters
- template_filenamestr, optional
Name of an existing notebook file to build from
Returns
dict
- save_to(output_filename, template_filename=DefaultTemplate)
Save this class to a file as a jupyter notebook
Parameters
- output_filenamestr
File to save the output jupyter notebook to
- template_filenamestr, optional
Name of an existing notebook file to build from
Returns
None
- add_block(block, cell_type)
Add a block to the notebook
Parameters
- blockstr
block of either code or markdown
- cell_typestr
tag for the cell. Either ‘code’ or ‘markdown’
Returns
None
- add_file(filename, cell_type)
Read in a cell block from a file
Parameters
- filenamestr
filename containing either code or markdown
- cell_typestr
tag for the cell. Either ‘code’ or ‘markdown’
Returns
None
- add_markdown(block)
Add markdown to notebook
Parameters
- blockstr
Block of markdown (or HTML)
Returns
None
- add_code_file(filename)
Add a code file to the notebook
Parameters
- filenamestr
name of file containing python code
Returns
None
- add_markdown_file(filename)
Add a markdown file to the notebook
Parameters
- filenamestr
name of file containing markdown
Returns
None
- add_notebook_text(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
- textstr
notebook formatted text
Returns
None
- add_notebook_text_file(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
- filenamestr
name of file containing notebook formatted text
Returns
None
- add_notebook_text_files(filenames)
Add multiple notebook text files to the notebook, in order
Parameters
- filenameslist(str)
names of file containing notebook formatted text
Returns
None
- add_notebook_file(filename)
Append an .ipynb file to this notebook
Parameters
- filenamestr
ipynb file to append
Returns
None
- add_notebook_files(filenames)
Add multiple notebook files to the notebook, in order
Parameters
- filenameslist(str)
names of file containing ipynb json
Returns
None
- launch_new(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_filenamestr
filename to save this notebook to
- template_filenamestr, optional
filename to build this notebook from (see save_to)
Returns
None
- launch(output_filename, template_filename=DefaultTemplate, port='auto')
Save and then launch this notebook
Parameters
- output_filenamestr
filename to save this notebook to
- template_filenamestr, optional
filename to build this notebook from (see save_to)
- portint, 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
- templatesdict (str -> Path-like)
A map of the available report generation types (html, pdf, notebook) to template paths.
- resultsResults or similar
The underlying Results-like object used to generate this report.
- sectionsiterable of Section
Collection of sections to be built into the generated report.
- flagsset of str
Set of flags controlling aspects of report generation.
- global_qtysdict (str -> any)
Key-value map of report quantities not tied to any specific section.
- report_paramsdict (str -> any)
Key-value map of report quantities used when building sections.
- build_defaultsdict (str -> any), optional
Default values for the build_options parameter of this instance’s build methods. Defaults to an empty dict.
- pdf_availablebool, optional
Trueif the underlying results can be represented as a static PDF. If this report cannot be represented statically,write_pdfwill raise. Defaults toTrue.- workspaceWorkspace, optional
A
Workspaceused for caching figure computation. By default, a new workspace will be used.
- write_html(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
- pathstr or path-like 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_openbool, optional
Whether the output file should be automatically opened in a web browser.
- link_tolist, 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.
- 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.
- build_optionsdict
Dict of options for building plots. Expected values are defined during construction of this report object.
- brevityint, 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 per-sequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germ-level estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
- 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”).
- single_filebool, optional
If true, the report will be written to a single HTML document, with external dependencies baked-in. This mode is not recommended for large reports, because this file can grow large enough that major web browsers may struggle to render it.
- verbosityint, optional
Amount of detail to print to stdout.
- write_notebook(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.
Note that interactive cells in report notebooks require JavaScript, and therefore do not work with JupyterLab. Please continue to use classic Jupyter notebooks for PyGSTi report notebooks. To track this issue, see https://github.com/pyGSTio/pyGSTi/issues/205.
Parameters
- pathstr or path-like object
The filesystem path to write the report to. By convention, this should use the .ipynb file extension.
- auto_openbool, optional
If True, automatically open the report in a web browser after it has been generated.
- connectedbool, 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.
- verbosityint, optional
How much detail to send to stdout.
- write_pdf(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
- pathstr or path-like object
The filesystem path to write the report to. By convention, this should use the .pdf file extension.
- latex_cmdstr, 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_optionsdict
Dict of options for building plots. Expected values are defined during construction of this report object.
- brevityint, 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 per-sequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germ-level estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
- 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.
- auto_openbool, optional
Whether the output file should be automatically opened in a web browser.
- commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
- verbosityint, optional
Amount of detail to print to stdout.
- pygsti.report.FINITE_DIFF_EPS = 1e-07
- pygsti.report.evaluate(model_fn, cri=None, verbosity=0)
Evaluate a ModelFunction object using confidence region information
Parameters
- model_fnModelFunction
The function to evaluate
- criConfidenceRegionFactoryView, optional
View for computing confidence intervals.
- verbosityint, 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_vecslist
A list of
Stateobjects.- povmslist
A list of
POVMobjects.
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
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
- mx_basisBasis 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
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
- mx_basisBasis 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
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
- mx_basisBasis 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.ModelFunctionGate eigenvalues
Parameters
- modelModel
Model gate is contained within.
- oplabelLabel
The gate’s label within model.
Creates a new ModelFunction object.
Parameters
- modelModel
A sample model giving the constructor a template for what type/parameterization of model to expect in calls to
evaluate().- dependencieslist
A list of (type,*label*) tuples, or the special strings “all” and “spam”, indicating which Model parameters the function depends upon. Here type can be “gate”, “prep”, “povm”, or “instrument”, and label can be any of the corresponding labels found in the models being evaluated. The reason for specifying this at all is to speed up computation of the finite difference derivative needed to find the error bars.
- class pygsti.report.CircuitEigenvalues(model, circuit)
Bases:
pygsti.report.modelfunction.ModelFunctionCircuit eigenvalues
Parameters
- modelModel
Model used to evaluate circuit.
- circuitCircuit
The circuit whose process matrix we want the eigenvalues of.
Creates a new ModelFunction object.
Parameters
- modelModel
A sample model giving the constructor a template for what type/parameterization of model to expect in calls to
evaluate().- dependencieslist
A list of (type,*label*) tuples, or the special strings “all” and “spam”, indicating which Model parameters the function depends upon. Here type can be “gate”, “prep”, “povm”, or “instrument”, and label can be any of the corresponding labels found in the models being evaluated. The reason for specifying this at all is to speed up computation of the finite difference derivative needed to find the error bars.
- pygsti.report.rel_circuit_eigenvalues(model_a, model_b, circuit)
Eigenvalues of dot(productB(circuit)^-1, productA(circuit))
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
numpy.ndarray
- pygsti.report.Rel_circuit_eigenvalues
- pygsti.report.circuit_frobenius_diff(model_a, model_b, circuit)
Frobenius distance btwn productA(circuit) and productB(circuit)
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- pygsti.report.Circuit_fro_diff
- pygsti.report.circuit_entanglement_infidelity(model_a, model_b, circuit)
Entanglement infidelity btwn productA(circuit) and productB(circuit)
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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)
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- pygsti.report.Circuit_jt_diff
- class pygsti.report.CircuitHalfDiamondNorm(model_a, model_b, circuit)
Bases:
pygsti.report.modelfunction.ModelFunction1/2 diamond norm of difference between productA(circuit) and productB(circuit)
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Creates a new ModelFunction object.
Parameters
- modelModel
A sample model giving the constructor a template for what type/parameterization of model to expect in calls to
evaluate().- dependencieslist
A list of (type,*label*) tuples, or the special strings “all” and “spam”, indicating which Model parameters the function depends upon. Here type can be “gate”, “prep”, “povm”, or “instrument”, and label can be any of the corresponding labels found in the models being evaluated. The reason for specifying this at all is to speed up computation of the finite difference derivative needed to find the error bars.
- pygsti.report.circuit_nonunitary_entanglement_infidelity(model_a, model_b, circuit)
Nonunitary entanglement infidelity between productA(circuit) and productB(circuit)
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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).
Parameters
- model_aModel
The first model (to evaluate productA)
- model_bModel
The second model (to evaluate productB)
- circuitCircuit
The circuit.
Returns
float
- 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 k-outcomes to the space of (diagonal) k by k density matrices.
Parameters
- model_aModel
The first model.
- model_bModel
The second model.
- povmlblLabel
The POVM label (must be present in both models).
Returns
float
- 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 k-outcomes to the space of (diagonal) k by k density matrices.
Parameters
- model_aModel
The first model.
- model_bModel
The second model.
- povmlblLabel
The POVM label (must be present in both models).
Returns
float
- 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 k-outcomes to the space of (diagonal) k by k density matrices.
Parameters
- model_aModel
The first model.
- model_bModel
The second model.
- povmlblLabel
The POVM label (must be present in both models).
Returns
float
- pygsti.report.decomposition(gate)
DEPRECATED: Decompose a 1Q gate into rotations about axes.
Parameters
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
Returns
ReportableQty
- pygsti.report.upper_bound_fidelity(gate, mx_basis)
Upper bound on entanglement fidelity
Parameters
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
- mx_basisBasis 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
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
- mx_basisBasis 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
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
- mx_basisBasis 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
- gatenumpy.ndarray
the transfer-matrix specifying a gate’s action.
- mx_basisBasis 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
- modelModel
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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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.ModelFunctionHalf the diamond distance bewteen model_a.operations[op_label] and model_b.operations[op_label]
Parameters
- model_aModel
The first model.
- model_bModel
The second model.
- oplabelLabel
The operation (gate) label to compare.
Creates a new ModelFunction object.
Parameters
- modelModel
A sample model giving the constructor a template for what type/parameterization of model to expect in calls to
evaluate().- dependencieslist
A list of (type,*label*) tuples, or the special strings “all” and “spam”, indicating which Model parameters the function depends upon. Here type can be “gate”, “prep”, “povm”, or “instrument”, and label can be any of the corresponding labels found in the models being evaluated. The reason for specifying this at all is to speed up computation of the finite difference derivative needed to find the error bars.
- pygsti.report.std_unitarity(a, b, mx_basis)
a gauge-invariant quantity that behaves like the unitarity
Parameters
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis or {‘pp’, ‘gm’, ‘std’}
the basis that a and b are in.
Returns
float
- pygsti.report.eigenvalue_unitarity(a, b)
a gauge-invariant quantity that behaves like the unitarity
Parameters
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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 eigenvalue-unitarity of a*b^{-1}
Parameters
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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 eigenvalue-unitarity of a*b^{-1}
Parameters
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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 (1-qubit gates)
Parameters
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- errgennumpy.ndarray
The error generator.
- mx_basisBasis
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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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 gauge-robust technique.
Parameters
- model_aModel
The first model (A gates).
- model_bModel
The second model (B gates).
- synthetic_idle_circuitslist
Circuits that encode synthetic idles.
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.
Parameters
- model_aModel
The model.
- model_bModel
The target model.
Returns
dict
- pygsti.report.General_decomposition
- pygsti.report.average_gateset_infidelity(model_a, model_b)
Average model infidelity
Parameters
- model_aModel
The first model.
- model_bModel
The second model.
Returns
float
- 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
Parameters
- model_aModel
The first model.
- model_bModel
The second model.
Returns
float
- pygsti.report.Predicted_rb_number
- pygsti.report.vec_fidelity(a, b, mx_basis)
State fidelity between state vectors a and b
Parameters
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- anumpy.ndarray
The first process (transfer) matrix.
- bnumpy.ndarray
The second process (transfer) matrix.
- mx_basisBasis 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
- vecnumpy.ndarray
state vector as a 1D dense array.
- mx_basisBasis 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
- vecnumpy.ndarray
state vector as a 1D dense array.
- mx_basisBasis 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 human-readable name and tooltip for a given gate-function abbreviation.
Parameters
- namestr
An appreviation for a gate-function name. Allowed values are:
“inf” : entanglement infidelity
“agi” : average gate infidelity
“trace” : 1/2 trace distance
“diamond” : 1/2 diamond norm distance
“nuinf” : non-unitary entanglement infidelity
“nuagi” : non-unitary entanglement infidelity
“evinf” : eigenvalue entanglement infidelity
“evagi” : eigenvalue average gate infidelity
“evnuinf” : eigenvalue non-unitary entanglement infidelity
“evnuagi” : eigenvalue non-unitary entanglement infidelity
“evdiamond” : eigenvalue 1/2 diamond norm distance
“evnudiamond” : eigenvalue non-unitary 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 gate-function named by the abbreviation name.
Parameters
- namestr
An appreviation for a operation-function name. Allowed values are the same as those of
info_of_opfn_by_name().- modelModel
The model used by the operation-function.
- target_modelModel
The target model.
- op_label_or_stringstr 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_infoConfidenceRegion, optional
If not None, specifies a confidence-region used to compute error intervals.
Returns
ReportableQty
- pygsti.report.instrument_infidelity(a, b, mx_basis)
Infidelity between instruments a and b
Parameters
- aInstrument
The first instrument.
- bInstrument
The second instrument.
- mx_basisBasis 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
- aInstrument
The first instrument.
- bInstrument
The second instrument.
- mx_basisBasis 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 instrument-function named by the abbreviation name.
Parameters
- namestr
An appreviation for a operation-function name. Allowed values are the same as those of
info_of_opfn_by_name().- modelModel
The model used by the operation-function.
- target_modelModel
The target model.
- inst_labelLabel
The instrument label to compare.
- confidence_region_infoConfidenceRegion, optional
If not None, specifies a confidence-region used to compute error intervals.
Returns
ReportableQty
- class pygsti.report.Workspace(cachefile=None)
Bases:
objectCentral 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
- cachefilestr, optional
filename with cached workspace results
Initialize a Workspace object.
Parameters
- cachefilestr, optional
filename with cached workspace results
- save_cache(cachefile, show_unpickled=False)
Save this Workspace’s cache to a file.
Parameters
- cachefilestr
The filename to save the cache to.
- show_unpickledbool, optional
Whether to print quantities (keys) of cache that could not be saved because they were not pickle-able.
Returns
None
- load_cache(cachefile)
Load this Workspace’s cache from cachefile.
Parameters
- cachefilestr
The filename to load the cache from.
Returns
None
- init_notebook_mode(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
- connectedbool , 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.
- autodisplaybool , optional
Whether to automatically display workspace objects after they are created.
Returns
None
- switched_compute(fn, *args)
Calls a compute function with special handling of
SwitchedValuearguments.This is similar to calling fn, given its name and arguments, when some or all of those arguments are
SwitchedValueobjects.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
- fnfunction
The function to evaluate
Returns
- fn_valueslist
The function return values for all relevant sets of arguments. Denote the length of this list by N.
- switchboardslist
A list of all the relevant Switchboards used during the function evaluation. Denote the length of this list by M.
- switchboard_switch_indiceslist
A list of length M whose elements are tuples containing the 0-based indices of the relevant switches (i.e. those used by any of the arguments) for each switchboard (element of switchboards).
- switchpos_mapdict
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 0-based switch-position 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.
- class pygsti.report.PrimitiveOpsSingleScaleWildcardBudget(primitive_op_labels, reference_values=None, alpha=0, idle_name=None, reference_name=None)
Bases:
PrimitiveOpsWildcardBudgetBaseA wildcard budget containing a single scaling parameter.
This type of wildcard budget has a single parameter, and sets the wildcard error of each primitive op to be this scale parameter multiplied by a fixed reference value for the primitive op.
Typically, the reference values are chosen to be a modeled metric of gate quality, such as a gate’s gauge-optimized diamond distance to its target gate. Then, once a feasible wildcard error vector is found, the scaling parameter is the fractional increase of the metric (e.g. the diamond distance) of each primitive op needed to reconcile the model and data.
Parameters
- primitive_op_labelslist
A list of primitive-operation labels, e.g. Label(‘Gx’,(0,)), which give all the possible primitive ops (components of circuit layers) that will appear in circuits.
- reference_valueslist, optional
A list of the reference values for each primitive op, in the same order as primitive_op_list.
- alphafloat, optional
The initial value of the single scaling parameter that multiplies the reference values of each op to give the wildcard error that op.
- idle_namestr, optional
The gate name to be used for the 1-qubit idle gate. If not None, then circuit budgets are computed by considering layers of the circuit as being “padded” with 1-qubit idles gates on any empty lines.
Create a new WildcardBudget.
Parameters
- w_vecnumpy array
The “wildcard vector” which stores the parameters of this budget which can be varied when trying to find an optimal budget (similar to the parameters of a
Model).
- property alpha
- pygsti.report.ROBUST_SUFFIX_LIST = ['.robust', '.Robust', '.robust+', '.Robust+']
- pygsti.report.DEFAULT_NONMARK_ERRBAR_THRESHOLD = 100000.0
- 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_dirstr, optional
The directory in which “offline.zip” should be place.
Returns
None
- pygsti.report.create_general_report(results, filename, title='auto', confidence_level=None, linlog_percentile=5, errgen_type='logGTi', nmthreshold=DEFAULT_NONMARK_ERRBAR_THRESHOLD, 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
Reportobject.
- 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 HTML-mode, switches (drop-down boxes, buttons, etc.) allow the viewer to choose which estimate is displayed. The estimates in multiple
Resultsobjects 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 PDF-mode 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 highly-portable and self-contained 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
Reportobject.Parameters
- resultsResults
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.- filenamestring, 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).
- titlestring, optional
The title of the report. “auto” causes a random title to be generated (which you may or may not like).
- confidence_levelint, 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.
- commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
- wsWorkspace, 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_openbool, optional
If True, automatically open the report in a web browser after it has been generated.
- link_tolist, 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).
- brevityint, 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 per-sequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germ-level estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
- advanced_optionsdict, 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: {“logG-logT”, “logTiG”, “logGTi”}
The type of error generator to compute. Allowed values are:
“logG-logT” : 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 non-Markovian error bars. If None, then non-Markovian 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 non-robust 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) pauli-basis dictionaries, which map between 1-qubit 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)”.
- verbosityint, 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 n-qubit 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
CloudNoiseModelobjects 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
Reportobject.Parameters
- resultsResults
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.- filenamestring, 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).
- titlestring, optional
The title of the report. “auto” causes a random title to be generated (which you may or may not like).
- confidence_levelint, 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.
- commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
- wsWorkspace, 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_openbool, optional
If True, automatically open the report in a web browser after it has been generated.
- link_tolist, 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).
- brevityint, 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 per-sequences quantities disappear at brevity=1
2: Reference sections disappear at brevity=2
3: Germ-level estimate tables disappear at brevity=3
4: Everything but summary figures disappears at brevity=4
- advanced_optionsdict, 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 non-Markovian error bars. If None, then non-Markovian 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 non-robust 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)”.
- verbosityint, 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
- resultsResults
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.- filenamestring, optional
The output filename where the report file(s) will be saved. Must end in “.ipynb”.
- titlestring, optional
The title of the report. “auto” causes a random title to be generated (which you may or may not like).
- confidence_levelint, 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_openbool, optional
If True, automatically open the report in a web browser after it has been generated.
- connectedbool, 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.
- verbosityint, 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
- modelModel
The ideal (target) model of primitive gates.
- verbosityint, 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
- resultsResults
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.- titlestring, optional
The title of the report. “auto” causes a random title to be generated (which you may or may not like).
- confidence_levelint, 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.
- commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
- wsWorkspace, 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_optionsdict, 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 non-Markovian error bars. If None, then non-Markovian 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 non-robust counterpart when displayed in reports. (default is True).
- idt_basis_dictstuple, optional
Tuple of (prepDict,measDict) pauli-basis dictionaries, which map between 1-qubit 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).
- verbosityint, 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 n-qubit 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
CloudNoiseModelobjects or have similar structure.Parameters
- resultsResults
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.- titlestring, optional
The title of the report. “auto” causes a random title to be generated (which you may or may not like).
- confidence_levelint, 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.
- commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
- wsWorkspace, 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_optionsdict, 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 non-Markovian error bars. If None, then non-Markovian error bars are never computed.
- combine_robustbool, optional
Whether robust estimates should automatically be combined with their non-robust 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.
- verbosityint, 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
- resultsStabilityAnalysisResults
The drift-analysis results to create the report from.
- titlestring, optional
The title of the report. “auto” causes a random title to be generated (which you may or may not like).
- wsWorkspace, 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.
- verbosityint, optional
How much detail to send to stdout.
Returns
Report : A constructed report object
- 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
- figsizetuple or None, optional
The figure size.
- y_valueslist or None, optional
The y-axis values, typically corresponding to circuit widths.
- x_valueslist or None, optional
The x-axis values, typically corresponding to circuit depths.
- titlestring or None, optional
Plot title
- xlabelstring, optional
x-axis label
- ylabelstring, optional
y-axis label.
Return
fig, ax : matplolib fig and ax.
- pygsti.report.volumetric_plot(data, y_values=None, x_values=None, title=None, fig=None, ax=None, cmap=None, 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=None, title=None, cmap=None)
Creates volumetric benchmarking plots that display the maximum, mean and minimum of a given figure-of-merit (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
- vbdataframeVBDataFrame
A VBDataFrame object containing the data to be plotted in a VB plot.
- metricstring, 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.
- thresholdfloat, optional
The threshold for “success” for the figure-of-merit defined by metric. This threshold is used to compute the three “success” boundaries that are shown in the plot.
- hypothesis_teststring, 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.
- significancefloat, optional
The statistical significance in the hypothesis tests. Only used in hypothesis_test is not ‘none’.
- figsizetuple, optional
The figure size
- scaledict, optional
The scale for the three concentric squares, showing the maximum, mean and minimum. Defaults to {‘min’: 1.95, ‘mean’: 1, ‘max’: 0.13}.
- titlesting, optional
The figure title.
- cmapColorMap, optional
A matplotlib colormap.
Return
fig, ax : matplolib fig and ax.