pygsti.forwardsims.matrixforwardsim
Defines the MatrixForwardSimulator calculator class
Module Contents
Classes
A forward simulator that uses matrixmatrix products to compute circuit outcome probabilities. 

Computes circuit outcome probabilities by multiplying together circuitlayer process matrices. 
 class pygsti.forwardsims.matrixforwardsim.SimpleMatrixForwardSimulator(model=None)
Bases:
pygsti.forwardsims.forwardsim.ForwardSimulator
A forward simulator that uses matrixmatrix products to compute circuit outcome probabilities.
This is “simple” in that it adds a minimal implementation to its
ForwardSimulator
base class. Because of this, it lacks some of the efficiency of aMatrixForwardSimulator
object, and is mainly useful as a reference implementation and check for other simulators. product(circuit, scale=False)
Compute the product of a specified sequence of operation labels.
Note: LinearOperator matrices are multiplied in the reversed order of the tuple. That is, the first element of circuit can be thought of as the first gate operation performed, which is on the far right of the product of matrices.
Parameters
 circuitCircuit or tuple of operation labels
The sequence of operation labels.
 scalebool, optional
When True, return a scaling factor (see below).
Returns
 productnumpy array
The product or scaled product of the operation matrices.
 scalefloat
Only returned when scale == True, in which case the actual product == product * scale. The purpose of this is to allow a trace or other linear operation to be done prior to the scaling.
 dproduct(circuit, flat=False, wrt_filter=None)
Compute the derivative of a specified sequence of operation labels.
Parameters
 circuitCircuit or tuple of operation labels
The sequence of operation labels.
 flatbool, optional
Affects the shape of the returned derivative array (see below).
 wrt_filterlist of ints, optional
If not None, a list of integers specifying which gate parameters to include in the derivative. Each element is an index into an array of gate parameters ordered by concatenating each gate’s parameters (in the order specified by the model). This argument is used internally for distributing derivative calculations across multiple processors.
Returns
 derivnumpy array
if flat == False, a M x G x G array, where:
M == length of the vectorized model (number of model parameters)
G == the linear dimension of a operation matrix (G x G operation matrices).
and deriv[i,j,k] holds the derivative of the (j,k)th entry of the product with respect to the ith model parameter.
if flat == True, a N x M array, where:
N == the number of entries in a single flattened gate (ordering as numpy.flatten)
M == length of the vectorized model (number of model parameters)
and deriv[i,j] holds the derivative of the ith entry of the flattened product with respect to the jth model parameter.
 hproduct(circuit, flat=False, wrt_filter1=None, wrt_filter2=None)
Compute the hessian of a specified sequence of operation labels.
Parameters
 circuitCircuit or tuple of operation labels
The sequence of operation labels.
 flatbool, optional
Affects the shape of the returned derivative array (see below).
 wrt_filter1list of ints, optional
If not None, a list of integers specifying which parameters to differentiate with respect to in the first (row) derivative operations. Each element is an modelparameter index. This argument is used internally for distributing derivative calculations across multiple processors.
 wrt_filter2list of ints, optional
If not None, a list of integers specifying which parameters to differentiate with respect to in the second (col) derivative operations. Each element is an modelparameter index. This argument is used internally for distributing derivative calculations across multiple processors.
Returns
 hessiannumpy array
if flat == False, a M x M x G x G numpy array, where:
M == length of the vectorized model (number of model parameters)
G == the linear dimension of a operation matrix (G x G operation matrices).
and hessian[i,j,k,l] holds the derivative of the (k,l)th entry of the product with respect to the jth then ith model parameters.
if flat == True, a N x M x M numpy array, where:
N == the number of entries in a single flattened gate (ordered as numpy.flatten)
M == length of the vectorized model (number of model parameters)
and hessian[i,j,k] holds the derivative of the ith entry of the flattened product with respect to the kth then kth model parameters.
 class pygsti.forwardsims.matrixforwardsim.MatrixForwardSimulator(model=None, distribute_by_timestamp=False, num_atoms=None, processor_grid=None, param_blk_sizes=None)
Bases:
pygsti.forwardsims.distforwardsim.DistributableForwardSimulator
,SimpleMatrixForwardSimulator
Computes circuit outcome probabilities by multiplying together circuitlayer process matrices.
Interfaces with a model via its circuit_layer_operator method and extracts a dense matrix representation of operators by calling their to_dense method. An “evaluation tree” that composes all of the circuits using pairwise “joins” is constructed by a
MatrixCOPALayout
layout object, and this tree then directs pairwise multiplications of process matrices to compute circuit outcome probabilities. Derivatives are computed analytically, using operators’ deriv_wrt_params methods.Parameters
 modelModel, optional
The parent model of this simulator. It’s fine if this is None at first, but it will need to be set (by assigning self.model before using this simulator.
 distribute_by_timestampbool, optional
When True, treat the data as time dependent, and distribute the computation of outcome probabilitiesby assigning groups of processors to the distinct time stamps within the dataset. This means of distribution be used only when the circuits themselves contain no time delay infomation (all circuit layer durations are 0), as operators are cached at the “start” time of each circuit, i.e., the timestamp in the data set. If False, then the data is treated in a timeindependent way, and the overall counts for each outcome are used. If support for intracircuit time dependence is needed, you must use a different forward simulator (e.g.
MapForwardSimulator
). num_atomsint, optional
The number of atoms (subevaluationtrees) to use when creating the layout (i.e. when calling
create_layout()
). This determines how many units the element (circuit outcome probability) dimension is divided into, and doesn’t have to correclate with the number of processors. When multiple processors are used, if num_atoms is less than the number of processors then num_atoms should divide the number of processors evenly, so that num_atoms // num_procs groups of processors can be used to divide the computation over parameter dimensions. processor_gridtuple optional
Specifies how the total number of processors should be divided into a number of atomprocessors, 1stparameterderivprocessors, and 2ndparameterderivprocessors. Each level of specification is optional, so this can be a 1, 2, or 3 tuple of integers (or None). Multiplying the elements of processor_grid together should give at most the total number of processors.
 param_blk_sizestuple, optional
The parameter block sizes along the first or first & second parameter dimensions  so this can be a 0, 1 or 2tuple of integers or None values. A block size of None means that there should be no division into blocks, and that each block processor computes all of its parameter indices at once.
 create_layout(circuits, dataset=None, resource_alloc=None, array_types=('E',), derivative_dimensions=None, verbosity=0)
Constructs an circuitoutcomeprobabilityarray (COPA) layout for a list of circuits.
Parameters
 circuitslist
The circuits whose outcome probabilities should be included in the layout.
 datasetDataSet
The source of data counts that will be compared to the circuit outcome probabilities. The computed outcome probabilities are limited to those with counts present in dataset.
 resource_allocResourceAllocation
A available resources and allocation information. These factors influence how the layout (evaluation strategy) is constructed.
 array_typestuple, optional
A tuple of stringvalued array types. See
ForwardSimulator.create_layout()
. derivative_dimensionsint or tuple[int], optional
Optionally, the parameterspace dimension used when taking first and second derivatives with respect to the cirucit outcome probabilities. This must be nonNone when array_types contains ‘ep’ or ‘epp’ types. If a tuple, then must be length 1.
 verbosityint or VerbosityPrinter
Determines how much output to send to stdout. 0 means no output, higher integers mean more output.
Returns
MatrixCOPALayout
 bulk_product(circuits, scale=False, resource_alloc=None)
Compute the products of many circuits at once.
Parameters
 circuitslist of Circuits
The circuits to compute products for. These should not have any preparation or measurement layers.
 scalebool, optional
When True, return a scaling factor (see below).
 resource_allocResourceAllocation
Available resources for this computation. Includes the number of processors (MPI comm) and memory limit.
Returns
 prodsnumpy array
Array of shape S x G x G, where:  S == the number of operation sequences  G == the linear dimension of a operation matrix (G x G operation matrices).
 scaleValuesnumpy array
Only returned when scale == True. A lengthS array specifying the scaling that needs to be applied to the resulting products (final_product[i] = scaleValues[i] * prods[i]).
 bulk_dproduct(circuits, flat=False, return_prods=False, scale=False, resource_alloc=None, wrt_filter=None)
Compute the derivative of a many operation sequences at once.
Parameters
 circuitslist of Circuits
The circuits to compute products for. These should not have any preparation or measurement layers.
 flatbool, optional
Affects the shape of the returned derivative array (see below).
 return_prodsbool, optional
when set to True, additionally return the probabilities.
 scalebool, optional
When True, return a scaling factor (see below).
 resource_allocResourceAllocation
Available resources for this computation. Includes the number of processors (MPI comm) and memory limit.
 wrt_filterlist of ints, optional
If not None, a list of integers specifying which gate parameters to include in the derivative. Each element is an index into an array of gate parameters ordered by concatenating each gate’s parameters (in the order specified by the model). This argument is used internally for distributing derivative calculations across multiple processors.
Returns
 derivsnumpy array
if flat == False, an array of shape S x M x G x G, where:  S == len(circuits)  M == the length of the vectorized model  G == the linear dimension of a operation matrix (G x G operation matrices) and derivs[i,j,k,l] holds the derivative of the (k,l)th entry of the ith operation sequence product with respect to the jth model parameter.
if flat == True, an array of shape S*N x M where:  N == the number of entries in a single flattened gate (ordering same as numpy.flatten),  S,M == as above, and deriv[i,j] holds the derivative of the (i % G^2)th entry of the (i / G^2)th flattened operation sequence product with respect to the jth model parameter.
 productsnumpy array
Only returned when return_prods == True. An array of shape S x G x G; products[i] is the ith operation sequence product.
 scaleValsnumpy array
Only returned when scale == True. An array of shape S such that scaleVals[i] contains the multiplicative scaling needed for the derivatives and/or products for the ith operation sequence.
 bulk_fill_timedep_chi2(array_to_fill, layout, ds_circuits, num_total_outcomes, dataset, min_prob_clip_for_weighting, prob_clip_interval, ds_cache=None)
Compute the chi2 contributions for an entire tree of circuits, allowing for time dependent operations.
Computation is performed by summing together the contributions for each time the circuit is run, as given by the timestamps in dataset.
Parameters
 array_to_fillnumpy ndarray
an alreadyallocated 1D numpy array of length equal to the total number of computed elements (i.e. layout.num_elements)
 layoutCircuitOutcomeProbabilityArrayLayout
A layout for array_to_fill, describing what circuit outcome each element corresponds to. Usually given by a prior call to
create_layout()
. ds_circuitslist of Circuits
the circuits to use as they should be queried from dataset (see below). This is typically the same list of circuits used to construct layout potentially with some aliases applied.
 num_total_outcomeslist or array
a list of the total number of possible outcomes for each circuit (so len(num_total_outcomes) == len(ds_circuits_to_use)). This is needed for handling sparse data, where dataset may not contain counts for all the possible outcomes of each circuit.
 datasetDataSet
the data set used to compute the chi2 contributions.
 min_prob_clip_for_weightingfloat, optional
Sets the minimum and maximum probability p allowed in the chi^2 weights: N/(p*(1p)) by clipping probability p values to lie within the interval [ min_prob_clip_for_weighting, 1min_prob_clip_for_weighting ].
 prob_clip_interval2tuple or None, optional
(min,max) values used to clip the predicted probabilities to. If None, no clipping is performed.
Returns
None
 bulk_fill_timedep_dchi2(array_to_fill, layout, ds_circuits, num_total_outcomes, dataset, min_prob_clip_for_weighting, prob_clip_interval, chi2_array_to_fill=None, ds_cache=None)
Compute the chi2 jacobian contributions for an entire tree of circuits, allowing for time dependent operations.
Similar to
bulk_fill_timedep_chi2()
but compute the jacobian of the summed chi2 contributions for each circuit with respect to the model’s parameters.Parameters
 array_to_fillnumpy ndarray
an alreadyallocated ExM numpy array where E is the total number of computed elements (i.e. layout.num_elements) and M is the number of model parameters.
 layoutCircuitOutcomeProbabilityArrayLayout
A layout for array_to_fill, describing what circuit outcome each element corresponds to. Usually given by a prior call to
create_layout()
. ds_circuitslist of Circuits
the circuits to use as they should be queried from dataset (see below). This is typically the same list of circuits used to construct layout potentially with some aliases applied.
 num_total_outcomeslist or array
a list of the total number of possible outcomes for each circuit (so len(num_total_outcomes) == len(ds_circuits_to_use)). This is needed for handling sparse data, where dataset may not contain counts for all the possible outcomes of each circuit.
 datasetDataSet
the data set used to compute the chi2 contributions.
 min_prob_clip_for_weightingfloat, optional
Sets the minimum and maximum probability p allowed in the chi^2 weights: N/(p*(1p)) by clipping probability p values to lie within the interval [ min_prob_clip_for_weighting, 1min_prob_clip_for_weighting ].
 prob_clip_interval2tuple or None, optional
(min,max) values used to clip the predicted probabilities to. If None, no clipping is performed.
 chi2_array_to_fillnumpy array, optional
when not None, an alreadyallocated lengthE numpy array that is filled with the percircuit chi2 contributions, just like in bulk_fill_timedep_chi2(…).
Returns
None
 bulk_fill_timedep_loglpp(array_to_fill, layout, ds_circuits, num_total_outcomes, dataset, min_prob_clip, radius, prob_clip_interval, ds_cache=None)
Compute the loglikelihood contributions (within the “poisson picture”) for an entire tree of circuits.
Computation is performed by summing together the contributions for each time the circuit is run, as given by the timestamps in dataset.
Parameters
 array_to_fillnumpy ndarray
an alreadyallocated 1D numpy array of length equal to the total number of computed elements (i.e. layout.num_elements)
 layoutCircuitOutcomeProbabilityArrayLayout
A layout for array_to_fill, describing what circuit outcome each element corresponds to. Usually given by a prior call to
create_layout()
. ds_circuitslist of Circuits
the circuits to use as they should be queried from dataset (see below). This is typically the same list of circuits used to construct layout potentially with some aliases applied.
 num_total_outcomeslist or array
a list of the total number of possible outcomes for each circuit (so len(num_total_outcomes) == len(ds_circuits_to_use)). This is needed for handling sparse data, where dataset may not contain counts for all the possible outcomes of each circuit.
 datasetDataSet
the data set used to compute the logl contributions.
 min_prob_clipfloat, optional
The minimum probability treated normally in the evaluation of the loglikelihood. A penalty function replaces the true loglikelihood for probabilities that lie below this threshold so that the loglikelihood never becomes undefined (which improves optimizer performance).
 radiusfloat, optional
Specifies the severity of rounding used to “patch” the zerofrequency terms of the loglikelihood.
 prob_clip_interval2tuple or None, optional
(min,max) values used to clip the predicted probabilities to. If None, no clipping is performed.
Returns
None
 bulk_fill_timedep_dloglpp(array_to_fill, layout, ds_circuits, num_total_outcomes, dataset, min_prob_clip, radius, prob_clip_interval, logl_array_to_fill=None, ds_cache=None)
Compute the (“poisson picture”)loglikelihood jacobian contributions for an entire tree of circuits.
Similar to
bulk_fill_timedep_loglpp()
but compute the jacobian of the summed logl (in posison picture) contributions for each circuit with respect to the model’s parameters.Parameters
 array_to_fillnumpy ndarray
an alreadyallocated ExM numpy array where E is the total number of computed elements (i.e. layout.num_elements) and M is the number of model parameters.
 layoutCircuitOutcomeProbabilityArrayLayout
A layout for array_to_fill, describing what circuit outcome each element corresponds to. Usually given by a prior call to
create_layout()
. ds_circuitslist of Circuits
the circuits to use as they should be queried from dataset (see below). This is typically the same list of circuits used to construct layout potentially with some aliases applied.
 num_total_outcomeslist or array
a list of the total number of possible outcomes for each circuit (so len(num_total_outcomes) == len(ds_circuits_to_use)). This is needed for handling sparse data, where dataset may not contain counts for all the possible outcomes of each circuit.
 datasetDataSet
the data set used to compute the logl contributions.
 min_prob_clipfloat
a regularization parameter for the loglikelihood objective function.
 radiusfloat
a regularization parameter for the loglikelihood objective function.
 prob_clip_interval2tuple or None, optional
(min,max) values used to clip the predicted probabilities to. If None, no clipping is performed.
 logl_array_to_fillnumpy array, optional
when not None, an alreadyallocated lengthE numpy array that is filled with the percircuit logl contributions, just like in bulk_fill_timedep_loglpp(…).
Returns
None