pygsti.forwardsims.mapforwardsim

Defines the MapForwardSimulator calculator class

Module Contents

Classes

SimpleMapForwardSimulator

A forward simulator that uses matrix-vector products to compute circuit outcome probabilities.

MapForwardSimulator

Computes circuit outcome probabilities using circuit layer maps that act on a state.

Attributes

_dummy_profiler

_SUPEROP

_UNITARY

CLIFFORD

pygsti.forwardsims.mapforwardsim._dummy_profiler
pygsti.forwardsims.mapforwardsim._SUPEROP = 0
pygsti.forwardsims.mapforwardsim._UNITARY = 1
pygsti.forwardsims.mapforwardsim.CLIFFORD = 2
class pygsti.forwardsims.mapforwardsim.SimpleMapForwardSimulator(model=None)

Bases: pygsti.forwardsims.forwardsim.ForwardSimulator

A forward simulator that uses matrix-vector 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 a MapForwardSimulator object, and is mainly useful as a reference implementation and check for other simulators.

_compute_circuit_outcome_probabilities(self, array_to_fill, circuit, outcomes, resource_alloc, time=None)
_set_evotype(self, evotype)

Called when the evotype being used (defined by the parent model) changes. evotype will be None when the current model is None

__getstate__(self)
class pygsti.forwardsims.mapforwardsim.MapForwardSimulator(model=None, max_cache_size=0, num_atoms=None, processor_grid=None, param_blk_sizes=None)

Bases: pygsti.forwardsims.distforwardsim.DistributableForwardSimulator, SimpleMapForwardSimulator

Computes circuit outcome probabilities using circuit layer maps that act on a state.

Interfaces with a model via its circuit_layer_operator method and applies the resulting operators in order to propagate states and finally compute outcome probabilities. Derivatives are computed using finite-differences, and the prefix tables construbed by MapCOPALayout layout object are used to avoid duplicating (some) computation.

Parameters
  • model (Model, 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.

  • max_cache_size (int, optional) – The maximum number of “prefix” quantum states that may be cached for performance (within the layout). If None, there is no limit to how large the cache may be.

  • num_atoms (int, optional) – The number of atoms (sub-prefix-tables) to use when creating the layout (i.e. when calling :method:`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_grid (tuple optional) – Specifies how the total number of processors should be divided into a number of atom-processors, 1st-parameter-deriv-processors, and 2nd-parameter-deriv-processors. 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_sizes (tuple, optional) – The parameter block sizes along the first or first & second parameter dimensions - so this can be a 0-, 1- or 2-tuple 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.

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

Return a shallow copy of this MapForwardSimulator

Returns

MapForwardSimulator

create_layout(self, circuits, dataset=None, resource_alloc=None, array_types=('E',), derivative_dimension=None, verbosity=0)

Constructs an circuit-outcome-probability-array (COPA) layout for a list of circuits.

Parameters
  • circuits (list) – The circuits whose outcome probabilities should be included in the layout.

  • dataset (DataSet) – 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_alloc (ResourceAllocation) – A available resources and allocation information. These factors influence how the layout (evaluation strategy) is constructed.

  • array_types (tuple, optional) – A tuple of string-valued array types. See :method:`ForwardSimulator.create_layout`.

  • derivative_dimension (int, optional) – Optionally, the parameter-space dimension used when taking first and second derivatives with respect to the cirucit outcome probabilities. This must be non-None when array_types contains ‘ep’ or ‘epp’ types.

  • verbosity (int or VerbosityPrinter) – Determines how much output to send to stdout. 0 means no output, higher integers mean more output.

Returns

MapCOPALayout

_bulk_fill_probs_atom(self, array_to_fill, layout_atom, resource_alloc)
_bulk_fill_dprobs_atom(self, array_to_fill, dest_param_slice, layout_atom, param_slice, resource_alloc)
_bulk_fill_hprobs_atom(self, array_to_fill, dest_param_slice1, dest_param_slice2, layout_atom, param_slice1, param_slice2, resource_alloc)
_mapfill_hprobs_atom(self, array_to_fill, dest_indices, dest_param_indices1, dest_param_indices2, layout_atom, param_indices1, param_indices2, resource_alloc)

Helper function for populating hessian values by block.

bulk_fill_timedep_chi2(self, 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_fill (numpy ndarray) – an already-allocated 1D numpy array of length equal to the total number of computed elements (i.e. layout.num_elements)

  • layout (CircuitOutcomeProbabilityArrayLayout) – A layout for array_to_fill, describing what circuit outcome each element corresponds to. Usually given by a prior call to :method:`create_layout`.

  • ds_circuits (list 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_outcomes (list 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.

  • dataset (DataSet) – the data set used to compute the chi2 contributions.

  • min_prob_clip_for_weighting (float, optional) – Sets the minimum and maximum probability p allowed in the chi^2 weights: N/(p*(1-p)) by clipping probability p values to lie within the interval [ min_prob_clip_for_weighting, 1-min_prob_clip_for_weighting ].

  • prob_clip_interval (2-tuple 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(self, 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 :method:`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_fill (numpy ndarray) – an already-allocated 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.

  • layout (CircuitOutcomeProbabilityArrayLayout) – A layout for array_to_fill, describing what circuit outcome each element corresponds to. Usually given by a prior call to :method:`create_layout`.

  • ds_circuits (list 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_outcomes (list 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.

  • dataset (DataSet) – the data set used to compute the chi2 contributions.

  • min_prob_clip_for_weighting (float, optional) – Sets the minimum and maximum probability p allowed in the chi^2 weights: N/(p*(1-p)) by clipping probability p values to lie within the interval [ min_prob_clip_for_weighting, 1-min_prob_clip_for_weighting ].

  • prob_clip_interval (2-tuple or None, optional) – (min,max) values used to clip the predicted probabilities to. If None, no clipping is performed.

  • chi2_array_to_fill (numpy array, optional) – when not None, an already-allocated length-E numpy array that is filled with the per-circuit chi2 contributions, just like in bulk_fill_timedep_chi2(…).

Returns

None

bulk_fill_timedep_loglpp(self, array_to_fill, layout, ds_circuits, num_total_outcomes, dataset, min_prob_clip, radius, prob_clip_interval, ds_cache=None)

Compute the log-likelihood 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 already-allocated 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 :method:`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 log-likelihood. A penalty function replaces the true log-likelihood for probabilities that lie below this threshold so that the log-likelihood never becomes undefined (which improves optimizer performance).

radiusfloat, optional

Specifies the severity of rounding used to “patch” the zero-frequency terms of the log-likelihood.

prob_clip_interval2-tuple 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(self, 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”)log-likelihood jacobian contributions for an entire tree of circuits.

Similar to :method:`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_fill (numpy ndarray) – an already-allocated 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.

  • layout (CircuitOutcomeProbabilityArrayLayout) – A layout for array_to_fill, describing what circuit outcome each element corresponds to. Usually given by a prior call to :method:`create_layout`.

  • ds_circuits (list 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_outcomes (list 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.

  • dataset (DataSet) – the data set used to compute the logl contributions.

  • min_prob_clip (float) – a regularization parameter for the log-likelihood objective function.

  • radius (float) – a regularization parameter for the log-likelihood objective function.

  • prob_clip_interval (2-tuple or None, optional) – (min,max) values used to clip the predicted probabilities to. If None, no clipping is performed.

  • logl_array_to_fill (numpy array, optional) – when not None, an already-allocated length-E numpy array that is filled with the per-circuit logl contributions, just like in bulk_fill_timedep_loglpp(…).

Returns

None