pygsti.baseobjs

A sub-package holding utility objects

Subpackages

Submodules

Package Contents

Classes

SmartCache

Cache object that profiles itself

VerbosityPrinter

Class responsible for logging things to stdout or a file.

Profiler

Profiler objects are used for tracking both time and memory usage.

Basis

An ordered set of labeled matrices/vectors.

BuiltinBasis

A basis that is included within and integrated into pyGSTi.

ExplicitBasis

A Basis whose elements are specified directly.

TensorProdBasis

A Basis that is the tensor product of one or more "component" bases.

DirectSumBasis

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

Label

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

CircuitLabel

A (sub-)circuit label.

NicelySerializable

The base class for all "nicely serializable" objects in pyGSTi.

OutcomeLabelDict

An ordered dictionary of outcome labels, whose keys are tuple-valued outcome labels.

StateSpace

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

QubitSpace

A state space consisting of N qubits.

ExplicitStateSpace

A customizable definition of a state space.

ResourceAllocation

Describes available resources and how they should be allocated.

QubitGraph

A directed or undirected graph data structure used to represent geometrical layouts of qubits or qubit gates.

ElementaryErrorgenBasis

A basis for error-generator space defined by a set of elementary error generators.

ExplicitElementaryErrorgenBasis

A basis for error-generator space defined by a set of elementary error generators.

CompleteElementaryErrorgenBasis

Spanned by the elementary error generators of given type(s) (e.g. "Hamiltonian" and/or "other")

ErrorgenSpace

A vector space of error generators, spanned by some basis.

class pygsti.baseobjs.SmartCache(decorating=(None, None))

Bases: object

Cache object that profiles itself

Parameters

decorating (tuple) – module and function being decorated by the smart cache

StaticCacheList

A list of all SmartCache instances.

Type

list

StaticCacheList = []
__setstate__(self, d)
__getstate__(self)
__pygsti_getstate__(self)
add_digest(self, custom)

Add a “custom” digest function, used for hashing otherwise un-hashable types.

Parameters

custom (function) – A hashing function, which takes two arguments: md5 (a running MD5 hash) and val (the value to be hashed). It should call md5.update to add to the running hash, and needn’t return anything.

Returns

None

low_overhead_cached_compute(self, fn, arg_vals, kwargs=None)

Cached compute with less profiling. See :method:`cached_compute` docstring.

Parameters
  • fn (function) – Cached function

  • arg_vals (tuple or list) – Arguments to cached function

  • kwargs (dictionary) – Keyword arguments to cached function

Returns

  • key (the key used to hash the function call)

  • result (result of fn called with arg_vals and kwargs)

cached_compute(self, fn, arg_vals, kwargs=None)

Shows effectiveness of a cache

Parameters
  • fn (function) – Cached function

  • arg_vals (tuple or list) – Arguments to cached function

  • kwargs (dictionary) – Keyword arguments to cached function

Returns

  • key (the key used to hash the function call)

  • result (result of fn called with arg_vals and kwargs)

static global_status(printer)

Show the statuses of all Cache objects

Parameters

printer (VerbosityPrinter) – The printer to use for output.

Returns

None

avg_timedict(self, d)

Given a dictionary of lists of times (d), returns a dict of the summed times.

Parameters

d (dict) – A dictionary whose values are lists of times.

Returns

dict

status(self, printer)

Show the status of a cache object instance

Parameters

printer (VerbosityPrinter) – The printer to use for output.

Returns

None

class pygsti.baseobjs.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 higher

printer.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 non-root ranks) are stored.

Type

str

_comm_file_name

root filename for comm files (outputs of non-root ranks).

Type

str

_comm_file_ext

filename extension for comm files (outputs of non-root 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 on-screen 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 0-based 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 text-format 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 bar-filling character

  • empty_char (str, optional) – replaces ‘-’ as the empty-bar 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

class pygsti.baseobjs.Profiler(comm=None, default_print_memcheck=False)

Bases: object

Profiler objects are used for tracking both time and memory usage.

Parameters
  • comm (mpi4py.MPI.Comm optional) – The active MPI communicator.

  • default_print_memcheck (bool, optional) – Whether to print memory checks.

add_time(self, name, start_time, prefix=0)

Adds an elapsed time to a named “timer”-type accumulator.

Parameters
  • name (string) – The name of the timer to add elapsed time into (if the name doesn’t exist, one is created and initialized to the elapsed time).

  • start_time (float) – The starting time used to compute the elapsed, i.e. the value time.time()-start_time, which is added to the named timer.

  • prefix (int, optional) – Prefix to the timer name the current stack depth and this number of function names, starting with the current function and moving the call stack. When zero, no prefix is added. For example, with prefix == 1, “Total” might map to ” 3: myFunc: Total”.

Returns

None

add_count(self, name, inc=1, prefix=0)

Adds a given value to a named “counter”-type accumulator.

Parameters
  • name (string) – The name of the counter to add val into (if the name doesn’t exist, one is created and initialized to val).

  • inc (int, optional) – The increment (the value to add to the counter).

  • prefix (int, optional) – Prefix to the timer name the current stack depth and this number of function names, starting with the current function and moving the call stack. When zero, no prefix is added. For example, with prefix == 1, “Total” might map to ” 3: myFunc: Total”.

Returns

None

memory_check(self, name, printme=None, prefix=0)

Record the memory usage at this point and tag with a name.

Parameters
  • name (string) – The name of the memory checkpoint. (Later, memory information can be organized by checkpoint name.)

  • printme (bool, optional) – Whether or not to print the memory usage during this function call (if None, the default, then the value of default_print_memcheck specified during Profiler construction is used).

  • prefix (int, optional) – Prefix to the timer name the current stack depth and this number of function names, starting with the current function and moving the call stack. When zero, no prefix is added. For example, with prefix == 1, “Total” might map to ” 3: myFunc: Total”.

Returns

None

print_memory(self, name, show_minmax=False)

Prints the current memory usage (but doesn’t store it).

Useful for debugging, this function prints the current memory usage - optionally giving the mininum, maximum, and average across all the processors.

Parameters
  • name (string) – A label to print before the memory usage number(s).

  • show_minmax (bool, optional) – If True and there are multiple processors, print the min, average, and max memory usage from among the processors. Note that this will invoke MPI collective communication and so this print_memory call must be executed by all the processors. If False and there are multiple processors, only the rank 0 processor prints output.

Returns

None

print_message(self, msg, all_ranks=False)

Prints a message to stdout, possibly from all ranks.

A utility function used in debugging, this function offers a convenient way to print a message on only the root processor or on all processors.

Parameters
  • msg (string) – The message to print.

  • all_ranks (bool, optional) – If True, all processors will print msg, preceded by their rank label (e.g. “Rank4: “). If False, only the rank 0 processor will print the message.

Returns

None

_format_times(self, sort_by='name')

Formats a string to report the timer values recorded in this Profiler.

Parameters

sort_by ({"name","time"}) – What to sort list of timers by.

Returns

str

_format_counts(self, sort_by='name')

Formats a string to report the counter values recorded in this Profiler.

Parameters

sort_by ({"name","count"}) – What to sort list of counts by.

Returns

str

_format_memory(self, sort_by='name')

Formats a string to report the memory usage checkpoints recorded in this Profiler.

Parameters

sort_by ({"name","usage","timestamp"}) – What to sort list of counts by.

Returns

str

__getstate__(self)
__setstate__(self, state_dict)
class pygsti.baseobjs.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 vector-element. 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 non-simple 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 2-element 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 length-4 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 length-2 [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 vector-elements) in the basis.

Type

int

elshape

The shape of each element. Typically either a length-1 or length-2 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 direct-sum-of-tensor-product-blocks 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 tensor-product 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 vector-elements) in the basis.

property elshape(self)

The shape of each element. Typically either a length-1 or length-2 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 flattened-element 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 built-in 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 built-in 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_name-analogue of the standard basis that this basis’s elements are expressed in.

Parameters

builtin_basis_name (str, optional) – The name of the built-in 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 component-free version of the same builtin-basis 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.baseobjs.BuiltinBasis(name, dim_or_statespace, sparse=False)

Bases: LazyBasis

A basis that is included within and integrated into pyGSTi.

Such bases may, in most cases be represented merely by its name. (In actuality, a dimension is also required, but this is often able to be inferred from context.)

Parameters
  • name ({"pp", "gm", "std", "qt", "id", "cl", "sv"}) – Name of the basis to be created.

  • dim_or_statespace (int or StateSpace) – The dimension of the basis to be created or the state space for which a basis should be created. Note that when this is an integer it is the dimension of the vectors, which correspond to flattened elements in simple cases. Thus, a 1-qubit basis would have dimension 2 in the state-vector (name=”sv”) case and dimension 4 when constructing a density-matrix basis (e.g. name=”pp”).

  • sparse (bool, optional) – Whether basis elements should be stored as SciPy CSR sparse matrices or dense numpy arrays (the default).

_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 vector-elements) in the basis.

property elshape(self)

The shape of each element. Typically either a length-1 or length-2 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_elements(self)
_lazy_build_labels(self)
_copy_with_toggled_sparsity(self)
__eq__(self, other)

Return self==value.

class pygsti.baseobjs.ExplicitBasis(elements, labels=None, name=None, longname=None, real=False, sparse=None)

Bases: Basis

A Basis whose elements are specified directly.

All explicit bases are simple: their vector space is always taken to be that of the the flattened elements.

Parameters
  • elements (numpy.ndarray) – The basis elements (sometimes different from the vectors)

  • labels (list) – The basis labels

  • name (str, optional) – The name of this basis. If None, then a name will be automatically generated.

  • longname (str, optional) – A more descriptive name for this basis. If None, then the short name will be used.

  • real (bool, optional) – Whether the coefficients in the expression of an arbitrary vector as a linear combination of this basis’s elements must be real.

  • sparse (bool, optional) – Whether the elements of this Basis are stored as sparse matrices or vectors. If None, then this is automatically determined by the type of the initial object: elements[0] (sparse=False is used when len(elements) == 0).

Count

The number of custom bases, used for serialized naming

Type

int

Count = 0
_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 vector-elements) in the basis.

property elshape(self)

The shape of each element. Typically either a length-1 or length-2 tuple, corresponding to vector or matrix elements, respectively. Note that vector elements always have shape (dim,) (or (dim,1) in the sparse case).

_copy_with_toggled_sparsity(self)
__hash__(self)

Return hash(self).

class pygsti.baseobjs.TensorProdBasis(component_bases, name=None, longname=None)

Bases: LazyBasis

A Basis that is the tensor product of one or more “component” bases.

The elements of a TensorProdBasis consist of all tensor products of component basis elements (respecting the order given). The components of a TensorProdBasis must be simple bases so that kronecker products can be used to produce the parent basis’s elements.

A TensorProdBasis is a “simple” basis in that its flattened elements do correspond to its vectors.

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.

_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 vector-elements) in the basis.

property elshape(self)

The shape of each element. Typically either a length-1 or length-2 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_elements(self)
_lazy_build_labels(self)
_copy_with_toggled_sparsity(self)
__eq__(self, other)

Return self==value.

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

TensorProdBasis

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_name-analogue of the standard basis that this basis’s elements are expressed in.

Parameters

builtin_basis_name (str, optional) – The name of the built-in 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 component-free version of the same builtin-basis type.

Returns

Basis

class pygsti.baseobjs.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 block-diagonal 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 vector-elements) in the basis.

property elshape(self)

The shape of each element. Typically either a length-1 or length-2 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_name-analogue of the standard basis that this basis’s elements are expressed in.

Parameters

builtin_basis_name (str, optional) – The name of the built-in 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 component-free version of the same builtin-basis type.

Returns

Basis

class pygsti.baseobjs.Label

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 sector-names specifying which qubits, or more generally, parts of the Hilbert space that is acted upon by an object so-labeled.

property depth(self)

The depth of this label, viewed as a sub-circuit.

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 sub-circuits 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.baseobjs.CircuitLabel

Bases: Label, tuple

A (sub-)circuit label.

This class encapsulates a complete circuit as a single layer. It lacks some of the methods and metadata of a true Circuit object, but contains the essentials: the tuple of layer labels (held as the label’s components) and line labels (held as the label’s state-space labels)

__hash__
property name(self)

This label’s name (a string).

property sslbls(self)

This label’s state-space labels, often qubit labels (a tuple).

property reps(self)

Number of repetitions (of this label’s components) that this label represents.

property args(self)

This label’s arguments.

property components(self)

The sub-label components of this label, or just (self,) if no sub-labels exist.

property qubits(self)

An alias for sslbls, since commonly these are just qubit indices. (a tuple)

property num_qubits(self)

The number of qubits this label “acts” on (an integer). None if self.ssbls is None.

has_prefix(self, prefix, typ='all')

Whether this label has the given prefix.

Usually used to test whether the label names a given type.

Parameters
  • prefix (str) – The prefix to check for.

  • typ ({"any","all"}) – Whether, when there are multiple parts to the label, the prefix must occur in any or all of the parts.

Returns

bool

map_state_space_labels(self, mapper)

Apply a mapping to this Label’s state-space (qubit) labels.

Return a copy of this Label with all of the state-space labels (often just qubit labels) updated according to a mapping function.

For example, calling this function with mapper = {0: 1, 1: 3} on the Label “Gcnot:0:1” would return “Gcnot:1:3”.

Parameters

mapper (dict or function) – A dictionary whose keys are the existing state-space-label values and whose value are the new labels, or a function which takes a single (existing state-space-label) argument and returns a new state-space-label.

Returns

CircuitLabel

abstract strip_args(self)
__str__(self)

Defines how a Label is printed out, e.g. Gx:0 or Gcnot:1:2

__repr__(self)

Return repr(self).

abstract __add__(self, s)

Return self+value.

__eq__(self, other)

Defines equality between gates, so that they are equal if their values are equal.

__lt__(self, x)

Return self<value.

__gt__(self, x)

Return self>value.

__pygsti_reduce__(self)
__reduce__(self)

Helper for pickle.

__contains__(self, x)

Return key in self.

to_native(self)

Returns this label as native python types.

Useful for faster serialization.

Returns

tuple

replace_name(self, oldname, newname)

Returns a label with oldname replaced by newname.

Parameters
  • oldname (str) – Name to find.

  • newname (str) – Name to replace found name with.

Returns

CircuitLabel

is_simple(self)

Whether this is a “simple” (opaque w/a true name, from a circuit perspective) label or not.

Returns

bool

property depth(self)

The depth of this label, viewed as a sub-circuit.

expand_subcircuits(self)

Expand any sub-circuits 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.baseobjs.NicelySerializable

Bases: object

The base class for all “nicely serializable” objects in pyGSTi.

A “nicely serializable” object can be converted to and created from a native Python object (like a string or dict) that contains only other native Python objects. In addition, there are constraints on the makeup of these objects so that they can be easily serialized to standard text-based formats, e.g. JSON. For example, dictionary keys must be strings, and the list vs. tuple distinction cannot be assumed to be preserved during serialization.

classmethod read(cls, path, format=None)

Read an object of this type, or a subclass of this type, from a file.

Parameters
  • path (str or Path or file-like) – The filename to open or an already open input stream.

  • format ({'json', None}) – The format of the file. If None this is determined automatically by the filename extension of a given path.

Returns

NicelySerializable

classmethod load(cls, f, format='json')

Load an object of this type, or a subclass of this type, from an input stream.

Parameters
  • f (file-like) – An open input stream to read from.

  • format ({'json'}) – The format of the input stream data.

Returns

NicelySerializable

classmethod loads(cls, s, format='json')

Load an object of this type, or a subclass of this type, from a string.

Parameters
  • s (str) – The serialized object.

  • format ({'json'}) – The format of the string data.

Returns

NicelySerializable

classmethod from_nice_serialization(cls, state)

Create and initialize an object from a “nice” serialization.

A “nice” serialization here means one created by a prior call to to_nice_serialization using this class or a subclass of it. Nice serializations adhere to additional rules (e.g. that dictionary keys must be strings) that make them amenable to common file formats (e.g. JSON).

The state argument is typically a dictionary containing ‘module’ and ‘state’ keys specifying the type of object that should be created. This type must be this class or a subclass of it.

Parameters

state (object) – An object, usually a dictionary, representing the object to de-serialize.

Returns

object

to_nice_serialization(self)

Serialize this object in a way that adheres to “niceness” rules of common text file formats.

Returns

object – Usually a dictionary representing the serialized object, but may also be another native Python type, e.g. a string or list.

write(self, path, **format_kwargs)

Writes this object to a file.

Parameters
  • path (str or Path) – The name of the file that is written.

  • format ({'json', 'repr'}) – The format to write.

  • format_kwargs (dict, optional) – Additional arguments specific to the format being used. For example, the JSON format accepts indent as an argument because json.dump does.

Returns

None

dump(self, f, format='json', **format_kwargs)

Serializes and writes this object to a given output stream.

Parameters
  • f (file-like) – A writable output stream.

  • format ({'json', 'repr'}) – The format to write.

  • format_kwargs (dict, optional) – Additional arguments specific to the format being used. For example, the JSON format accepts indent as an argument because json.dump does.

Returns

None

dumps(self, format='json', **format_kwargs)

Serializes this object and returns it as a string.

Parameters
  • format ({'json', 'repr'}) – The format to write.

  • format_kwargs (dict, optional) – Additional arguments specific to the format being used. For example, the JSON format accepts indent as an argument because json.dump does.

Returns

str

_dump_or_dumps(self, f, format='json', **format_kwargs)

Serializes and writes this object to a given output stream.

Parameters
  • f (file-like) – A writable output stream. If None, then object is written as a string and returned.

  • format ({'json', 'repr'}) – The format to write.

  • format_kwargs (dict, optional) – Additional arguments specific to the format being used. For example, the JSON format accepts indent as an argument because json.dump does.

Returns

str or None – If f is None, then the serialized object as a string is returned. Otherwise, None is returned.

_to_nice_serialization(self)
classmethod _from_nice_serialization(cls, state)
classmethod _state_class(cls, state, check_is_subclass=True)

Returns the class specified by the given state dictionary

classmethod _check_compatible_nice_state(cls, state)
classmethod _encodemx(cls, mx)
classmethod _decodemx(cls, mx)
classmethod _encodevalue(cls, val)
classmethod _decodevalue(cls, val)
class pygsti.baseobjs.OutcomeLabelDict(items=[])

Bases: collections.OrderedDict

An ordered dictionary of outcome labels, whose keys are tuple-valued outcome labels.

This class extends an ordinary OrderedDict by implements mapping string-values single-outcome labels to 1-tuples containing that label (and vice versa), allowing the use of strings as outcomes labels from the user’s perspective.

Parameters

items (list or dict, optional) – Initial values. Should only be used as part of de-serialization.

_strict

Whether mapping from strings to 1-tuples is performed.

Type

bool

_strict = False
classmethod to_outcome(cls, val)

Converts string outcomes like “0” to proper outcome tuples, like (“0”,).

(also converts non-tuples to tuples, e.g. [“0”,”1”] to (“0”,”1”) )

Parameters

val (str or tuple) – The value to convert into an outcome label (i.e. a tuple)

Returns

tuple

__getitem__(self, key)

x.__getitem__(y) <==> x[y]

__setitem__(self, key, val)

Set self[key] to value.

getitem_unsafe(self, key, defaultval)

Gets an item without checking that key is a properly formatted outcome tuple.

Only use this method when you’re sure key is an outcome tuple and not, e.g., just a string.

Parameters
  • key (object) – The key to retrieve

  • defaultval (object) – The default value to use (if the key is absent).

Returns

object

setitem_unsafe(self, key, val)

Sets item without checking that the key is a properly formatted outcome tuple.

Only use this method when you’re sure key is an outcome tuple and not, e.g., just a string.

Parameters
  • key (object) – The key to retrieve.

  • val (object) – the value to set.

Returns

None

__contains__(self, key)

True if the dictionary has the specified key, else False.

contains_unsafe(self, key)

Checks for key without ensuring that it is a properly formatted outcome tuple.

Only use this method when you’re sure key is an outcome tuple and not, e.g., just a string.

Parameters

key (object) – The key to retrieve.

Returns

bool

copy(self)

Return a copy of this OutcomeLabelDict.

Returns

OutcomeLabelDict

__pygsti_reduce__(self)
__reduce__(self)

Helper for pickle.

class pygsti.baseobjs.StateSpace

Bases: pygsti.baseobjs.nicelyserializable.NicelySerializable

Base class for defining a state space (Hilbert or Hilbert-Schmidt 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 already-built 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 Hilbert-Schmidt (super-operator) 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 tensor-product blocks which are direct-summed to get the final state space.

Returns

int

property tensor_product_blocks_labels(self)

The labels for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_dimensions(self)

The superoperator dimensions for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_udimensions(self)

The unitary operator dimensions for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_types(self)

The type (quantum vs classical) of all the tensor-product 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 iTBP-th tensor-product block.

Parameters

i_tpb (int) – Tensor-product block index.

Returns

tuple

tensor_product_block_dimensions(self, i_tpb)

The superoperator dimensions for the factors in the iTBP-th tensor-product block.

Parameters

i_tpb (int) – Tensor-product block index.

Returns

tuple

tensor_product_block_udimensions(self, i_tpb)

The unitary-operator dimensions for the factors in the iTBP-th tensor-product block.

Parameters

i_tpb (int) – Tensor-product 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 super-op 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 unitary-op 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 sub-StateSpace 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 sub-StateSpace object from a set of potentially stencil-type 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.baseobjs.QubitSpace(nqubits_or_labels)

Bases: StateSpace

A state space consisting of N qubits.

_to_nice_serialization(self)
classmethod _from_nice_serialization(cls, state)
property udim(self)

Integer Hilbert (unitary operator) space dimension of this quantum state space.

property dim(self)

Integer Hilbert-Schmidt (super-operator) or classical dimension of this state space.

property num_qubits(self)

The number of qubits in this quantum state space.

property num_tensor_product_blocks(self)

Get the number of tensor-product blocks which are direct-summed to get the final state space.

Returns

int

property tensor_product_blocks_labels(self)

Get the labels for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_dimensions(self)

Get the superoperator dimensions for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_udimensions(self)

Get the unitary operator dimensions for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_types(self)

Get the type (quantum vs classical) of all the tensor-product blocks.

Returns

tuple of tuples

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

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

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

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

__str__(self)

Return str(self).

class pygsti.baseobjs.ExplicitStateSpace(label_list, udims=None, types=None)

Bases: StateSpace

A customizable definition of a state space.

An ExplicitStateSpace object describes, using string/int labels, how an entire Hilbert state space is decomposed into the direct sum of terms which themselves are tensor products of smaller (typically qubit-sized) Hilbert spaces.

Parameters
  • label_list (str or int or iterable) –

    Most generally, this can be a list of tuples, where each tuple contains the state-space labels (which can be strings or integers) for a single “tensor product block” formed by taking the tensor product of the spaces asociated with the labels. The full state space is the direct sum of all the tensor product blocks. E.g. [(‘Q0’,’Q1’), (‘Q2’,)].

    If just an iterable of labels is given, e.g. (‘Q0’,’Q1’), it is assumed to specify the first and only tensor product block.

    If a single state space label is given, e.g. ‘Q2’, then it is assumed to completely specify the first and only tensor product block.

  • udims (int or iterable, optional) –

    The dimension of each state space label as an integer, tuple of integers, or list or tuples of integers to match the structure of label_list (i.e., if label_list=(‘Q0’,’Q1’) then dims should be a tuple of 2 integers). Values specify unitary evolution state-space dimensions, i.e. 2 for a qubit, 3 for a qutrit, etc. If None, then the dimensions are inferred, if possible, from the following naming rules:

    • if the label starts with ‘L’, udim=1 (a single Level)

    • if the label starts with ‘Q’ OR is an int, udim=2 (a Qubit)

    • if the label starts with ‘T’, udim=3 (a quTrit)

  • types (str or iterable, optional) – A list of label types, either ‘Q’ or ‘C’ for “quantum” and “classical” respectively, indicating the type of state-space associated with each label. Like dims, types must match the structure of label_list. A quantum state space of dimension d is a d-by-d density matrix, whereas a classical state space of dimension d is a vector of d probabilities. If None, then all labels are assumed to be quantum.

_to_nice_serialization(self)
classmethod _from_nice_serialization(cls, state)
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 Hilbert-Schmidt (super-operator) 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 tensor-product blocks which are direct-summed to get the final state space.

Returns

int

property tensor_product_blocks_labels(self)

The labels for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_dimensions(self)

The superoperator dimensions for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_udimensions(self)

The unitary operator dimensions for all the tensor-product blocks.

Returns

tuple of tuples

property tensor_product_blocks_types(self)

The type (quantum vs classical) of all the tensor-product blocks.

Returns

tuple of tuples

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

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

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

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

__str__(self)

Return str(self).

class pygsti.baseobjs.ResourceAllocation(comm=None, mem_limit=None, profiler=None, distribute_method='default', allocated_memory=0)

Bases: object

Describes available resources and how they should be allocated.

This includes the number of processors and amount of memory, as well as a strategy for how computations should be distributed among them.

Parameters
  • comm (mpi4py.MPI.Comm, optional) – MPI communicator holding the number of available processors.

  • mem_limit (int, optional) – A rough per-processor memory limit in bytes.

  • profiler (Profiler, optional) – A lightweight profiler object for tracking resource usage.

  • distribute_method (str, optional) – The name of a distribution strategy.

classmethod cast(cls, arg)

Cast arg to a ResourceAllocation object.

If arg already is a ResourceAllocation instance, it just returned. Otherwise this function attempts to create a new instance from arg.

Parameters

arg (ResourceAllocation or dict) – An object that can be cast to a ResourceAllocation.

Returns

ResourceAllocation

build_hostcomms(self)
property comm_rank(self)

A safe way to get self.comm.rank (0 if self.comm is None)

property comm_size(self)

A safe way to get self.comm.size (1 if self.comm is None)

property is_host_leader(self)

True if this processors is the rank-0 “leader” of its host (node). False otherwise.

host_comm_barrier(self)

Calls self.host_comm.barrier() when self.host_comm is not None.

This convenience function provides an often-used barrier that follows code where a single “leader” processor modifies a memory block shared between all members of self.host_comm, and the other processors must wait until this modification is performed before proceeding with their own computations.

Returns

None

copy(self)

Copy this object.

Returns

ResourceAllocation

reset(self, allocated_memory=0)

Resets internal allocation counters to given values (defaults to zero).

Parameters

allocated_memory (int64) – The value to set the memory allocation counter to.

Returns

None

add_tracked_memory(self, num_elements, dtype='d')

Adds nelements * itemsize bytes to the total amount of allocated memory being tracked.

If the total (tracked) memory exceeds self.mem_limit a MemoryError exception is raised.

Parameters
  • num_elements (int) – The number of elements to track allocation of.

  • dtype (numpy.dtype, optional) – The type of elements, needed to compute the number of bytes per element.

Returns

None

check_can_allocate_memory(self, num_elements, dtype='d')

Checks that allocating nelements doesn’t cause the memory limit to be exceeded.

This memory isn’t tracked - it’s just added to the current tracked memory and a MemoryError exception is raised if the result exceeds self.mem_limit.

Parameters
  • num_elements (int) – The number of elements to track allocation of.

  • dtype (numpy.dtype, optional) – The type of elements, needed to compute the number of bytes per element.

Returns

None

temporarily_track_memory(self, num_elements, dtype='d')

Temporarily adds nelements to tracked memory (a context manager).

A MemoryError exception is raised if the tracked memory exceeds self.mem_limit.

Parameters
  • num_elements (int) – The number of elements to track allocation of.

  • dtype (numpy.dtype, optional) – The type of elements, needed to compute the number of bytes per element.

Returns

contextmanager

gather_base(self, result, local, slice_of_global, unit_ralloc=None, all_gather=False)

Gather or all-gather operation using local arrays and a unit resource allocation.

Similar to a normal MPI gather call, but more easily integrates with a hierarchy of processor divisions, or nested comms, by taking a unit_ralloc argument. This is essentially another comm that specifies the groups of processors that have all computed the same local array, i.e., slice of the final to-be gathered array. So, when gathering the result, only processors with unit_ralloc.rank == 0 need to contribute to the gather operation.

Parameters
  • result (numpy.ndarray, possibly shared) – The destination “global” array. When shared memory is being used, i.e. when this ResourceAllocation object has a nontrivial inter-host comm, this array must be allocated as a shared array using this ralloc or a larger so that result is shared between all the processors for this resource allocation’s intra-host communicator. This allows a speedup when shared memory is used by having multiple smaller gather operations in parallel instead of one large gather.

  • local (numpy.ndarray) – The locally computed quantity. This can be a shared-memory array, but need not be.

  • slice_of_global (slice or numpy.ndarray) – The slice of result that local constitutes, i.e., in the end result[slice_of_global] = local. This may be a Python slice or a NumPy array of indices.

  • unit_ralloc (ResourceAllocation, optional) – A resource allocation (essentially a comm) for the group of processors that all compute the same local result, so that only the unit_ralloc.rank == 0 processors will contribute to the gather operation. If None, then it is assumed that all processors compute different local results.

  • all_gather (bool, optional) – Whether the final result should be gathered on all the processors of this ResourceAllocation or just the root (rank 0) processor.

Returns

None

gather(self, result, local, slice_of_global, unit_ralloc=None)

Gather local arrays into a global result array potentially with a unit resource allocation.

Similar to a normal MPI gather call, but more easily integrates with a hierarchy of processor divisions, or nested comms, by taking a unit_ralloc argument. This is essentially another comm that specifies the groups of processors that have all computed the same local array, i.e., slice of the final to-be gathered array. So, when gathering the result, only processors with unit_ralloc.rank == 0 need to contribute to the gather operation.

The global array is only gathered on the root (rank 0) processor of this resource allocation.

Parameters
  • result (numpy.ndarray, possibly shared) – The destination “global” array, only needed on the root (rank 0) processor. When shared memory is being used, i.e. when this ResourceAllocation object has a nontrivial inter-host comm, this array must be allocated as a shared array using this ralloc or a larger so that result is shared between all the processors for this resource allocation’s intra-host communicator. This allows a speedup when shared memory is used by having multiple smaller gather operations in parallel instead of one large gather.

  • local (numpy.ndarray) – The locally computed quantity. This can be a shared-memory array, but need not be.

  • slice_of_global (slice or numpy.ndarray) – The slice of result that local constitutes, i.e., in the end result[slice_of_global] = local. This may be a Python slice or a NumPy array of indices.

  • unit_ralloc (ResourceAllocation, optional) – A resource allocation (essentially a comm) for the group of processors that all compute the same local result, so that only the unit_ralloc.rank == 0 processors will contribute to the gather operation. If None, then it is assumed that all processors compute different local results.

Returns

None

allgather(self, result, local, slice_of_global, unit_ralloc=None)

All-gather local arrays into global arrays on each processor, potentially using a unit resource allocation.

Similar to a normal MPI gather call, but more easily integrates with a hierarchy of processor divisions, or nested comms, by taking a unit_ralloc argument. This is essentially another comm that specifies the groups of processors that have all computed the same local array, i.e., slice of the final to-be gathered array. So, when gathering the result, only processors with unit_ralloc.rank == 0 need to contribute to the gather operation.

Parameters
  • result (numpy.ndarray, possibly shared) – The destination “global” array. When shared memory is being used, i.e. when this ResourceAllocation object has a nontrivial inter-host comm, this array must be allocated as a shared array using this ralloc or a larger so that result is shared between all the processors for this resource allocation’s intra-host communicator. This allows a speedup when shared memory is used by having multiple smaller gather operations in parallel instead of one large gather.

  • local (numpy.ndarray) – The locally computed quantity. This can be a shared-memory array, but need not be.

  • slice_of_global (slice or numpy.ndarray) – The slice of result that local constitutes, i.e., in the end result[slice_of_global] = local. This may be a Python slice or a NumPy array of indices.

  • unit_ralloc (ResourceAllocation, optional) – A resource allocation (essentially a comm) for the group of processors that all compute the same local result, so that only the unit_ralloc.rank == 0 processors will contribute to the gather operation. If None, then it is assumed that all processors compute different local results.

Returns

None

allreduce_sum(self, result, local, unit_ralloc=None)

Sum local arrays on different processors, potentially using a unit resource allocation.

Similar to a normal MPI reduce call (with MPI.SUM type), but more easily integrates with a hierarchy of processor divisions, or nested comms, by taking a unit_ralloc argument. This is essentially another comm that specifies the groups of processors that have all computed the same local array. So, when performing the sum, only processors with unit_ralloc.rank == 0 contribute to the sum. This handles the case where simply summing the local contributions from all processors would result in over-counting because of multiple processors hold the same logical result (summand).

Parameters
  • result (numpy.ndarray, possibly shared) – The destination “global” array, with the same shape as all the local arrays being summed. This can be any shape (including any number of dimensions). When shared memory is being used, i.e. when this ResourceAllocation object has a nontrivial inter-host comm, this array must be allocated as a shared array using this ralloc or a larger so that result is shared between all the processors for this resource allocation’s intra-host communicator. This allows a speedup when shared memory is used by distributing computation of result over each host’s processors and performing these sums in parallel.

  • local (numpy.ndarray) – The locally computed quantity. This can be a shared-memory array, but need not be.

  • unit_ralloc (ResourceAllocation, optional) – A resource allocation (essentially a comm) for the group of processors that all compute the same local result, so that only the unit_ralloc.rank == 0 processors will contribute to the sum operation. If None, then it is assumed that all processors compute different local results.

Returns

None

allreduce_sum_simple(self, local, unit_ralloc=None)

A simplified sum over quantities on different processors that doesn’t use shared memory.

The shared memory usage of :method:`allreduce_sum` can be overkill when just summing a single scalar quantity. This method provides a way to easily sum a quantity across all the processors in this ResourceAllocation object using a unit resource allocation.

Parameters
  • local (int or float) – The local (per-processor) value to sum.

  • unit_ralloc (ResourceAllocation, optional) – A resource allocation (essentially a comm) for the group of processors that all compute the same local value, so that only the unit_ralloc.rank == 0 processors will contribute to the sum. If None, then it is assumed that each processor computes a logically different local value.

Returns

float or int – The sum of all local quantities, returned on all the processors.

allreduce_min(self, result, local, unit_ralloc=None)

Take elementwise min of local arrays on different processors, potentially using a unit resource allocation.

Similar to a normal MPI reduce call (with MPI.MIN type), but more easily integrates with a hierarchy of processor divisions, or nested comms, by taking a unit_ralloc argument. This is essentially another comm that specifies the groups of processors that have all computed the same local array. So, when performing the min operation, only processors with unit_ralloc.rank == 0 contribute.

Parameters
  • result (numpy.ndarray, possibly shared) – The destination “global” array, with the same shape as all the local arrays being operated on. This can be any shape (including any number of dimensions). When shared memory is being used, i.e. when this ResourceAllocation object has a nontrivial inter-host comm, this array must be allocated as a shared array using this ralloc or a larger so that result is shared between all the processors for this resource allocation’s intra-host communicator. This allows a speedup when shared memory is used by distributing computation of result over each host’s processors and performing these sums in parallel.

  • local (numpy.ndarray) – The locally computed quantity. This can be a shared-memory array, but need not be.

  • unit_ralloc (ResourceAllocation, optional) – A resource allocation (essentially a comm) for the group of processors that all compute the same local result, so that only the unit_ralloc.rank == 0 processors will contribute to the sum operation. If None, then it is assumed that all processors compute different local results.

Returns

None

allreduce_max(self, result, local, unit_ralloc=None)

Take elementwise max of local arrays on different processors, potentially using a unit resource allocation.

Similar to a normal MPI reduce call (with MPI.MAX type), but more easily integrates with a hierarchy of processor divisions, or nested comms, by taking a unit_ralloc argument. This is essentially another comm that specifies the groups of processors that have all computed the same local array. So, when performing the max operation, only processors with unit_ralloc.rank == 0 contribute.

Parameters
  • result (numpy.ndarray, possibly shared) – The destination “global” array, with the same shape as all the local arrays being operated on. This can be any shape (including any number of dimensions). When shared memory is being used, i.e. when this ResourceAllocation object has a nontrivial inter-host comm, this array must be allocated as a shared array using this ralloc or a larger so that result is shared between all the processors for this resource allocation’s intra-host communicator. This allows a speedup when shared memory is used by distributing computation of result over each host’s processors and performing these sums in parallel.

  • local (numpy.ndarray) – The locally computed quantity. This can be a shared-memory array, but need not be.

  • unit_ralloc (ResourceAllocation, optional) – A resource allocation (essentially a comm) for the group of processors that all compute the same local result, so that only the unit_ralloc.rank == 0 processors will contribute to the sum operation. If None, then it is assumed that all processors compute different local results.

Returns

None

bcast(self, value, root=0)

Broadcasts a value from the root processor/host to the others in this resource allocation.

This is similar to a usual MPI broadcast, except it takes advantage of shared memory when it is available. When shared memory is being used, i.e. when this ResourceAllocation object has a nontrivial inter-host comm, then this routine places value in a shared memory buffer and uses the resource allocation’s inter-host communicator to broadcast the result from the root host to all the other hosts using all the processor on the root host in parallel (all processors with the same intra-host rank participate in a MPI broadcast).

Parameters
  • value (numpy.ndarray) – The value to broadcast. May be shared memory but doesn’t need to be. Only need to specify this on the rank root processor, other processors can provide any value for this argument (it’s unused).

  • root (int) – The rank of the processor whose value will be to broadcast.

Returns

numpy.ndarray – The broadcast value, in a new, non-shared-memory array.

__getstate__(self)
class pygsti.baseobjs.QubitGraph(qubit_labels, initial_connectivity=None, initial_edges=None, directed=True, direction_names=None)

Bases: pygsti.baseobjs.nicelyserializable.NicelySerializable

A directed or undirected graph data structure used to represent geometrical layouts of qubits or qubit gates.

Qubits are nodes in the graph (and can be labeled), and edges represent the ability to perform one or more types of gates between qubits (equivalent, usually, to geometrical proximity).

Parameters
  • qubit_labels (list) – A list of string or integer labels of the qubits. The length of this list equals the number of qubits (nodes) in the graph.

  • initial_connectivity (numpy.ndarray, optional) – A (nqubits, nqubits) boolean or integer array giving the initial connectivity of the graph. If an integer array, then 0 indicates no edge and positive integers indicate present edges in the “direction” given by the positive integer. For example 1 may corresond to “left” and 2 to “right”. Names must be associated with these directions using direction_names. If a boolean array, if there’s an edge from qubit i to j then initial_connectivity[i,j]=True (integer indices of qubit labels are given by their position in qubit_labels). When directed=False, only the upper triangle is used.

  • initial_edges (list) – A list of (qubit_label1, qubit_label2) 2-tuples or (qubit_label1, qubit_label2, direction) 3-tuples specifying which edges are initially present. direction can either be a positive integer, similar to those used in initial_connectivity (in which case direction_names must be specified) or a string labeling the direction, e.g. “left”.

  • directed (bool, optional) – Whether the graph is directed or undirected. Directions can only be used when directed=True.

  • direction_names (iterable, optional) – A list (or tuple, etc) of string-valued direction names such as “left” or “right”. These strings label the directions referenced by index in either initial_connectivity or initial_edges, and this argument is required whenever such indices are used.

classmethod common_graph(cls, num_qubits=0, geometry='line', directed=True, qubit_labels=None, all_directions=False)

Create a QubitGraph that is one of several standard types of graphs.

Parameters
  • num_qubits (int, optional) – The number of qubits (nodes in the graph).

  • geometry ({"line","ring","grid","torus"}) – The type of graph. What these correspond to should be self-evident.

  • directed (bool, optional) – Whether the graph is directed or undirected.

  • qubit_labels (iterable, optional) – The labels for the qubits. Must be of length num_qubits. If None, then the integers from 0 to num_qubits-1 are used.

  • all_directions (bool, optional) – Whether to include edges with all directions. Typically it only makes sense to set this to True when directed=True also.

Returns

QubitGraph

map_qubit_labels(self, mapper)

Creates a new QubitGraph whose qubit (node) labels are updated according to the mapping function mapper.

Parameters

mapper (dict or function) – A dictionary whose keys are the existing self.node_names values and whose value are the new labels, or a function which takes a single (existing qubit-label) argument and returns a new qubit label.

Returns

QubitProcessorSpec

_to_nice_serialization(self)
classmethod _from_nice_serialization(cls, state)
copy(self)

Make a copy of this graph.

Returns

QubitGraph

_refresh_dists_and_predecessors(self)
__getitem__(self, key)
__setitem__(self, key, val)
__len__(self)
property node_names(self)

All the node labels of this graph.

These correpond to integer indices where appropriate, e.g. for :method:`shortest_path_distance_matrix`.

Returns

tuple

add_edges(self, edges)

Add edges (list of tuple pairs) to graph.

Parameters

edges (list) – A list of (qubit_label1, qubit_label2) 2-tuples.

Returns

None

add_edge(self, node1, node2, direction=None)

Add an edge between the qubits labeled by node1 and node2.

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

  • direction (str or int, optional) – Either a direction name or a direction indicex

Returns

None

remove_edge(self, node1, node2)

Add an edge between the qubits labeled by node1 and node2.

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

Returns

None

edges(self, double_for_undirected=False, include_directions=False)

Get a list of the edges in this graph as 2-tuples of node/qubit labels).

When undirected, the index of the 2-tuple’s first label will always be less than its second unless double_for_undirected == True, in which case both directed edges are included. The edges are sorted (by label index) in ascending order.

Parameters
  • double_for_undirected (bool, optional) – Whether, for the case of an undirected graph, two 2-tuples, giving both edge directions, should be included in the returned list.

  • include_directions (bool, optional) – Whether to include direction labels. If True and directions are present, a list of (node1, node2, direction_name) 3-tuples is returned instead of the usual (node1, node2) 2-tuples.

Returns

list

radius(self, base_nodes, max_hops)

Find all the nodes reachable in max_hops from any node in base_nodes.

Get a (sorted) array of node labels that can be reached from traversing at most max_hops edges starting from a node (vertex) in base_nodes.

Parameters
  • base_nodes (iterable) – A list of node/qubit labels giving the possible starting locations.

  • max_hops (int) – The maximum number of hops (see above).

Returns

list – A list of the node labels reachable from base_nodes in at most max_hops edge traversals.

connected_combos(self, possible_nodes, size)

Computes the number of different connected subsets of possible_nodes containing size nodes.

Parameters
  • possible_nodes (list) – A list of node (qubit) labels.

  • size (int) – The size of the connected subsets being sought (counted).

Returns

int

_indices_connected(self, i, j)

Whether nodes indexed by i and j are directly connected

is_connected(self, node1, node2)

Is node1 connected to node2 (does there exist a path of any length between them?)

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

Returns

bool

has_edge(self, edge)

Is edge an edge in this graph.

Note that if this graph is undirected, either node order in edge will return True.

Parameters

edge (tuple) – (node1,node2) tuple specifying the edge.

Returns

bool

is_directly_connected(self, node1, node2)

Is node1 directly connected to node2 (does there exist an edge between them?)

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

Returns

bool

_is_connected_subgraph(self, node_indices)

Whether the nodes indexed by the elements of node_indices form a connected subgraph.

is_connected_graph(self)

Computes whether this graph is connected (there exist paths between every pair of nodes).

Returns

bool

is_connected_subgraph(self, nodes)

Do a give set of nodes form a connected subgraph?

That is, does there exist a path from every node in nodes to every other node in nodes using only the edges between the nodes in nodes.

Parameters

nodes (list) – A list of node (qubit) labels.

Returns

bool

_brute_get_all_connected_sets(self, n)

Computes all connected sets of n qubits using a brute-force approach.

Note that for a large device with this will be often be an unreasonably large number of sets of qubits, and so the run-time of this method will be unreasonable.

Parameters

n (int) – The number of qubits within each set.

Returns

list – All sets of n connected qubits.

find_all_connected_sets(self)

Finds all subgraphs (connected sets of vertices) up to the full graph size.

Graph edges are treated as undirected.

Returns

dict – A dictionary with integer keys. The value of key k is a list of all the subgraphs of length k. A subgraph is given as a tuple of sorted vertex labels.

shortest_path(self, node1, node2)

Get the shortest path between two nodes (qubits).

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

Returns

list – A list of the node labels to traverse.

shortest_path_edges(self, node1, node2)

Like :method:`shortest_path`, but returns a list of (nodeA,nodeB) tuples.

These tuples define a path from node1 to node2, so the first tuple’s nodeA == node1 and the final tuple’s nodeB == node2.

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

Returns

list – A list of the edges (2-tuples of node labels) to traverse.

shortest_path_intersect(self, node1, node2, nodes_to_intersect)

Check whether the shortest path between node1 and node2 contains any of the nodes in nodes_to_intersect.

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

  • nodes_to_intersect (list) – A list of node labels.

Returns

bool – True if the shortest path intersects any node in nodeToIntersect.

shortest_path_distance(self, node1, node2)

Get the distance of the shortest path between node1 and node2.

Parameters
  • node1 (str or int) – Qubit (node) label.

  • node2 (str or int) – Qubit (node) label.

Returns

int

shortest_path_distance_matrix(self)

Returns a matrix of shortest path distances.

This matrix is indexed by the integer-index of each node label (as specified to __init__). The list of index-ordered node labels is given by :method:`node_names`.

Returns

numpy.ndarray – A boolean array of shape (n,n) where n is the number of nodes in this graph.

shortest_path_predecessor_matrix(self)

Returns a matrix of predecessors used to construct the shortest path between two nodes.

This matrix is indexed by the integer-index of each node label (as specified to __init__). The list of index-ordered node labels is given by :method:`node_names`.

Returns

numpy.ndarray – A boolean array of shape (n,n) where n is the number of nodes in this graph.

subgraph(self, nodes_to_keep, reset_nodes=False)

Return a graph that includes only nodes_to_keep and the edges between them.

Parameters
  • nodes_to_keep (list) – A list of node labels defining the subgraph to return.

  • reset_nodes (bool, optional) – If True, nodes of returned subgraph are relabelled to be the integers starting at 0 (in 1-1 correspondence with the ordering in nodes_to_keep).

Returns

QubitGraph

resolve_relative_nodelabel(self, relative_nodelabel, target_labels)

Resolve a “relative nodelabel” into an actual node in this graph.

Relative node labels can use “@” to index elements of target_labels and can contain “+<dir>” directives to move along directions defined in this graph.

Parameters
  • relative_nodelabel (int or str) – An absolute or relative node-label. For example: 0, “@0”, “@0+right”, “@1+left+up”

  • target_labels (list or tuple) – A list of (absolute) node labels present in this graph that may be referred to using the “@” syntax within relative_nodelabel.

Returns

int or str

move_in_directions(self, start_node, directions)

The node you end up on after moving in directions from start_node.

Parameters
  • start_node (str or int) – Qubit (node) label.

  • directions (iterable) – A sequence of direction names.

Returns

str or int or None – The ending node label or None if the directions were invalid.

move_in_direction(self, start_node, direction)

Get the node that is one step in direction of start_node.

Parameters
  • start_node (int or str) – the starting point (a node label of this graph)

  • direction (str or int) – the name of a direction or its index within this graphs .directions member.

Returns

str or int or None – the node in the given direction or None if there is no node in that direction (e.g. if you reach the end of a chain).

__str__(self)

Return str(self).

class pygsti.baseobjs.ElementaryErrorgenBasis

Bases: object

A basis for error-generator space defined by a set of elementary error generators.

Elements are ordered (have definite indices) and labeled. Intersection and union can be performed as a set.

label_indices(self, labels)

TODO: docstring

__len__(self)

Number of elementary errorgen elements in this basis

class pygsti.baseobjs.ExplicitElementaryErrorgenBasis(state_space, labels, basis1q=None)

Bases: ElementaryErrorgenBasis

A basis for error-generator space defined by a set of elementary error generators.

Elements are ordered (have definite indices) and labeled. Intersection and union can be performed as a set.

property labels(self)
property elemgen_supports_and_matrices(self)
label_index(self, label)

TODO: docstring

create_subbasis(self, must_overlap_with_these_sslbls)

Create a sub-basis of this basis by including only the elements that overlap the given support (state space labels)

union(self, other_basis)
intersection(self, other_basis)
difference(self, other_basis)
class pygsti.baseobjs.CompleteElementaryErrorgenBasis(basis_1q, state_space, other_mode, max_ham_weight=None, max_other_weight=None, must_overlap_with_these_sslbls=None)

Bases: ElementaryErrorgenBasis

Spanned by the elementary error generators of given type(s) (e.g. “Hamiltonian” and/or “other”) and with elements corresponding to a Basis, usually of Paulis.

classmethod _create_diag_labels_for_support(cls, support, type_str, nontrivial_bels)
classmethod _create_all_labels_for_support(cls, support, left_support, type_str, trivial_bel, nontrivial_bels)
classmethod _create_ordered_labels(cls, type_str, basis_1q, state_space, mode='diagonal', max_weight=None, must_overlap_with_these_sslbls=None, include_offsets=False)
classmethod _create_ordered_label_offsets(cls, type_str, basis_1q, state_space, mode='diagonal', max_weight=None, must_overlap_with_these_sslbls=None, return_total_support=False)

same as _create_ordered_labels but doesn’t actually create the labels - just counts them to get offsets.

__len__(self)

Number of elementary errorgen elements in this basis

to_explicit_basis(self)
property labels(self)
property elemgen_supports_and_matrices(self)
label_index(self, elemgen_label)

TODO: docstring

create_subbasis(self, must_overlap_with_these_sslbls, retain_max_weights=True)

Create a sub-basis of this basis by including only the elements that overlap the given support (state space labels)

union(self, other_basis)
intersection(self, other_basis)
difference(self, other_basis)
class pygsti.baseobjs.ErrorgenSpace(vectors, basis)

Bases: object

A vector space of error generators, spanned by some basis.

This object collects the information needed to specify a space within the space of all error generators.

intersection(self, other_space, free_on_unspecified_space=False)

TODO: docstring

abstract union(self, other_space)

TODO: docstring