pygsti.layouts.copalayout

A object representing the indexing into a (flat) array of circuit outcome probabilities.

Module Contents

Classes

CircuitOutcomeProbabilityArrayLayout

The arrangement of circuit outcome probabilities into a 1D array.

class pygsti.layouts.copalayout.CircuitOutcomeProbabilityArrayLayout(circuits, unique_circuits, to_unique, elindex_outcome_tuples, unique_complete_circuits=None, param_dimensions=(), resource_alloc=None)

Bases: pygsti.baseobjs.nicelyserializable.NicelySerializable

The arrangement of circuit outcome probabilities into a 1D array.

This class describes how the outcome probabilities for a list of circuits map to the elements of a one-dimensional array. Computation, e.g., of an objective function such as the log-likelihood, is performed using the 1D array, and the layout is then used to assign meaning to each of the array elements, i.e., to lookup which element corresponds to a given circuit and outcome.

This could be a simple concatenation of all the possible outcomes for each circuit in turn. However, when not all outcomes are observed it is unnecessary to compute the corresponding probabilities, and so this layout can be “sparse” in this sense.

When there are multiple processors, a layout may assign different outcome probabilities (and their derivatives) to different processors. Thus, a layout can be dependent on the available processors and holds (and “owns”) a ResourceAllocation object. This class creates a non-distributed layout that is simply duplicated across all the available processors.

Parameters

circuitslist of Circuits

The circuits whose outcome probabilities are to be computed. This list may contain duplicates.

unique_circuitslist of Circuits

The same as circuits, except duplicates are removed. Often this value is obtained by a derived class calling the class method _compute_unique_circuits().

to_uniquedict

A mapping that translates an index into circuits to one into unique_circuits. Keys are the integers 0 to len(circuits) and values are indices into unique_circuits.

elindex_outcome_tuplescollections.OrderedDict

A dictionary whose keys are integer indices into unique_circuits and whose values are lists of (element_index, outcome_label) tuples that give the element index within the 1D array of the probability (or other quantity) corresponding to the given circuit and outcome label. Note that outcome labels themselves are tuples of instrument/POVM member labels.

unique_complete_circuitslist, optional

A list, parallel to unique_circuits, that contains the “complete” version of these circuits. This information is currently unused, and is included for potential future expansion and flexibility.

param_dimensionstuple, optional

A tuple containing, optionally, the parameter-space dimension used when taking first and second derivatives with respect to the circuit outcome probabilities. This is meta-data bundled along with the main layout information, and is needed for allocating arrays with derivative dimensions.

resource_allocResourceAllocation, optional

The resources available for computing circuit outcome probabilities.

Attributes

num_elementsint

The total number of elements in this layout. In a multi-processor context, the number of elements locally owned by the current processor.

num_elementsint

The total number of circuits in this layout. In a multi-processor context, the number of circuits locally owned by the current processor.

global_layoutCircuitOutcomeProbabilityArrayLayout

A layout containing all the circuits in their original order, that is the same on all processors and doesn’t depend on a specific resource allocation. This is either the layout itself or a larger layout that this layout is a part of.

property num_elements

The total number of elements in this layout. In a multi-processor context, the number of elements locally owned by the current processor.

property num_circuits

The total number of circuits in this layout. In a multi-processor context, the number of circuits locally owned by the current processor.

property global_layout

A layout containing all the circuits in their original order, that is the same on all processors and doesn’t depend on a specific resource allocation. This is either the layout itself or a larger layout that this layout is a part of.

classmethod create_from(circuits, model=None, dataset=None, param_dimensions=(), resource_alloc=None)

Creates a simple layout from a list of circuits.

Optionally, a model can be used to “complete” (add implied prep or POVM layers) circuits, and a dataset to restrict the layout’s elements to the observed outcomes.

Parameters

circuitslist of Circuits

The circuits to include in the layout. Note that the produced layout may not retain the ordering of these circuits internally, but that it’s .global_layout does.

modelModel, optional

A model used to “complete” the circuits (add implied prep and/or POVM layers). Usually this is a/the model that will be used to compute outcomes probabilities using this layout. If None, then each element of circuits is assumed to be a complete circuit, i.e., to begin with a state preparation layer and end with a POVM layer.

datasetDataSet, optional

If not None, restrict what is simplified to only those probabilities corresponding to non-zero counts (observed outcomes) in this data set.

param_dimensionstuple, optional

A tuple containing, optionally, the parameter-space dimension used when taking first and second derivatives with respect to the circuit outcome probabilities.

resource_allocResourceAllocation, optional

The resources available for computing circuit outcome probabilities.

Returns

CircuitOutcomeProbabilityArrayLayout

allocate_local_array(array_type, dtype, zero_out=False, memory_tracker=None, extra_elements=0)

Allocate an array that is distributed according to this layout.

Creates an array for holding elements and/or derivatives with respect to model parameters, possibly distributed among multiple processors as dictated by this layout.

Parameters

array_type{“e”, “ep”, “ep2”, “epp”, “p”, “jtj”, “jtf”, “c”, “cp”, “cp2”, “cpp”}

The type of array to allocate, often corresponding to the array shape. Let nE be the layout’s number of elements, nP1 and nP2 be the number of parameters we differentiate with respect to (for first and second derivatives), and nC be the number of circuits. Then the array types designate the following array shapes: - “e”: (nE,) - “ep”: (nE, nP1) - “ep2”: (nE, nP2) - “epp”: (nE, nP1, nP2) - “p”: (nP1,) - “jtj”: (nP1, nP2) - “jtf”: (nP1,) - “c”: (nC,) - “cp”: (nC, nP1) - “cp2”: (nC, nP2) - “cpp”: (nC, nP1, nP2) Note that, even though the “p” and “jtf” types are the same shape they are used for different purposes and are distributed differently when there are multiple processors. The “p” type is for use with other element-dimentions-containing arrays, whereas the “jtf” type assumes that the element dimension has already been summed over.

dtypenumpy.dtype

The NumPy data type for the array.

zero_outbool, optional

Whether the array should be zeroed out initially.

memory_trackerResourceAllocation, optional

If not None, the amount of memory being allocated is added, using add_tracked_memory() to this resource allocation object.

extra_elementsint, optional

The number of additional “extra” elements to append to the element dimension, beyond those called for by this layout. Such additional elements are used to store penalty terms that are treated by the objective function just like usual outcome-probability-type terms.

Returns

numpy.ndarray

free_local_array(local_array)

Frees an array allocated by allocate_local_array().

This method should always be paired with a call to allocate_local_array(), since the allocated array may utilize shared memory, which must be explicitly de-allocated.

Parameters

local_arraynumpy.ndarray or LocalNumpyArray

The array to free, as returned from allocate_local_array.

Returns

None

gather_local_array_base(array_type, array_portion, extra_elements=0, all_gather=False, return_shared=False)

Gathers an array onto the root processor or all the processors.

Gathers the portions of an array that was distributed using this layout (i.e. according to the host_element_slice, etc. slices in this layout). This could be an array allocated by allocate_local_array() but need not be, as this routine does not require that array_portion be shared. Arrays can be 1, 2, or 3-dimensional. The dimensions are understood to be along the “element”, “parameter”, and “2nd parameter” directions in that order.

Parameters

array_type(“e”, “ep”, “ep2”, “epp”, “p”, “jtj”, “jtf”, “c”, “cp”, “cp2”, “cpp”)

The type of array to allocate, often corresponding to the array shape. See allocate_local_array() for a more detailed description.

array_portionnumpy.ndarray

The portion of the final array that is local to the calling processor. This could be a shared memory array, but just needs to be of the correct size.

extra_elementsint, optional

The number of additional “extra” elements to append to the element dimension, beyond those called for by this layout. Should match usage in allocate_local_array().

all_gatherbool, optional

Whether the result should be returned on all the processors (when all_gather=True) or just the rank-0 processor (when all_gather=False).

return_sharedbool, optional

Whether the returned array is allowed to be a shared-memory array, which results in a small performance gain because the array used internally to gather the results can be returned directly. When True a shared memory handle is also returned, and the caller assumes responsibilty for freeing the memory via pygsti.tools.sharedmemtools.cleanup_shared_ndarray().

Returns

gathered_arraynumpy.ndarray or None

The full (global) output array on the root (rank=0) processor and None on all other processors, unless all_gather == True, in which case the array is returned on all the processors.

shared_memory_handlemultiprocessing.shared_memory.SharedMemory or None

Returned only when return_shared == True. The shared memory handle associated with gathered_array, which is needed to free the memory.

gather_local_array(array_type, array_portion, extra_elements=0, return_shared=False)

Gathers an array onto the root processor.

Gathers the portions of an array that was distributed using this layout (i.e. according to the host_element_slice, etc. slices in this layout). This could be an array allocated by allocate_local_array() but need not be, as this routine does not require that array_portion be shared. Arrays can be 1, 2, or 3-dimensional. The dimensions are understood to be along the “element”, “parameter”, and “2nd parameter” directions in that order.

Parameters

array_portionnumpy.ndarray

The portion of the final array that is local to the calling processor. This could be a shared memory array, but just needs to be of the correct size.

extra_elementsint, optional

The number of additional “extra” elements to append to the element dimension, beyond those called for by this layout. Should match usage in allocate_local_array().

return_sharedbool, optional

If True then, when shared memory is being used, the shared array used to accumulate the gathered results is returned directly along with its shared-memory handle (None if shared memory isn’t used). This results in a small performance gain.

Returns

resultnumpy.ndarray or None

The full (global) output array on the root (rank=0) processor and None on all other processors.

shared_memory_handlemultiprocessing.shared_memory.SharedMemory or None

Returned only when return_shared == True. The shared memory handle associated with result, which is needed to free the memory.

allgather_local_array(array_type, array_portion, extra_elements=0, return_shared=False)

Gathers an array onto all the processors.

Gathers the portions of an array that was distributed using this layout (i.e. according to the host_element_slice, etc. slices in this layout). This could be an array allocated by allocate_local_array() but need not be, as this routine does not require that array_portion be shared. Arrays can be 1, 2, or 3-dimensional. The dimensions are understood to be along the “element”, “parameter”, and “2nd parameter” directions in that order.

Parameters

array_portionnumpy.ndarray

The portion of the final array that is local to the calling processor. This could be a shared memory array, but just needs to be of the correct size.

extra_elementsint, optional

The number of additional “extra” elements to append to the element dimension, beyond those called for by this layout. Should match usage in allocate_local_array().

return_sharedbool, optional

If True then, when shared memory is being used, the shared array used to accumulate the gathered results is returned directly along with its shared-memory handle (None if shared memory isn’t used). This results in a small performance gain.

Returns

resultnumpy.ndarray or None

The full (global) output array.

shared_memory_handlemultiprocessing.shared_memory.SharedMemory or None

Returned only when return_shared == True. The shared memory handle associated with result, which is needed to free the memory.

allsum_local_quantity(typ, value, use_shared_mem='auto')

Sum a local array (or scalar) distributed using this layout.

Sums per-element or per-circuit values across the processors used by this layout. Each array must be the same size, but need not be allocated in any particular way.

Parameters

typ{“e”, “c”}

Whether the array is an element or circuit array.

valuefloat or numpy.ndarray

The value to sum. Must be the same size on all processors.

use_shared_membool, optional

If True then, a tempoary shared memory array is allocated and used for the sum (when shared memory is available). For large arrays, using shared memory is faster than MPI communication, but for small arrays the overhead of creating the shared memory negates these gains. This argument must be False when value is just a float.

Returns

numpy.ndarray or float

The summed value, returned on all the processors.

fill_jtf(j, f, jtf)

Calculate the matrix-vector product j.T @ f.

Here j is often a jacobian matrix, and f a vector of objective function term values. j and f must be local arrays, created with allocate_local_array(). This function performs any necessary MPI/shared-memory communication when the arrays are distributed over multiple processors.

Parameters

jLocalNumpyArray

A local 2D array (matrix) allocated using allocate_local_array with the “ep” (jacobian) type.

fLocalNumpyArray

A local array allocated using allocate_local_array with the “e” (element array) type.

jtfLocalNumpyArray

The result. This must be a pre-allocated local array of type “jtf”.

Returns

None

fill_jtj(j, jtj)

Calculate the matrix-matrix product j.T @ j.

Here j is often a jacobian matrix, so the result is an approximate Hessian. This function performs any necessary MPI/shared-memory communication when the arrays are distributed over multiple processors.

Parameters

jLocalNumpyArray

A local 2D array (matrix) allocated using allocate_local_array with the “ep” (jacobian) type.

jtjLocalNumpyArray

The result. This must be a pre-allocated local array of type “jtj”.

Returns

None

memory_estimate(array_type, dtype='d')

Memory required to allocate an array of a given type (in bytes).

Parameters

array_type{‘e’, ‘ep’, ‘epp’}

The type of array. This string specifies the shape of the array, with ‘e’ indicating dimension holding the layout’s elements and ‘p’ indicating parameter dimensions.

dtypenumpy.dtype

The NumPy data type for the array.

Returns

int

The memory that would be required, in bytes.

indices(circuit)

The element indices corresponding to a circuit in this layout.

This is a slice into the element-dimension of arrays allocated using this layout, e.g. an ‘e’-type array allocated by allocate_local_array(). The entries of such an array correspond to different outcomes of this circuit, which are separately given by outcomes() or alongside the indices in indices_and_outcomes().

Parameters

circuitCircuit

The circuit to lookup element indices of.

Returns

slice

outcomes(circuit)

The outcome labels of a circuit in this layout.

Parameters

circuitCircuit

The circuit to lookup outcome labels of.

Returns

tuple

indices_and_outcomes(circuit)

The element indices and outcomes corresponding to a circuit in this layout.

Returns both the element indices and outcome labels corresponding to a circuit in this layout. These quantities can be separately obtained using the indices() and outcomes() methods, respectively.

Parameters

circuitCircuit

The circuit to lookup element indices and outcomes of.

Returns

element_indices : slice outcome_labels : tuple

indices_for_index(index)

Lookup the element indices corresponding to a given circuit by the circuit’s index.

Similar to indices() but uses a circuit’s index within this layout directly, thus avoiding having to hash a Circuit object and gaining a modicum of performance.

Parameters

indexint

The index of a circuit within this layout, i.e., within self.circuits.

Returns

slice

outcomes_for_index(index)

Lookup the outcomes of a given circuit by the circuit’s index.

Similar to outcomes() but uses a circuit’s index within this layout directly, thus avoiding having to hash a Circuit object and gaining a modicum of performance.

Parameters

indexint

The index of a circuit within this layout, i.e., within self.circuits.

Returns

tuple

indices_and_outcomes_for_index(index)

Lookup the element indices and outcomes corresponding to a given circuit by the circuit’s index.

Similar to indices_and_outcomes() but uses a circuit’s index within this layout directly, thus avoiding having to hash a Circuit object and gaining a modicum of performance.

Parameters

indexint

The index of a circuit within this layout, i.e., within self.circuits.

Returns

element_indices : slice outcome_labels : tuple

iter_unique_circuits()

Iterate over the element-indices, circuit, and outcomes of each unique circuit in this layout.

A generator used to iterate over a (element_indices, circuit, outcomes) tuple for each unique circuit held by this layout, where element_indices and outcomes are the values that would be retrieved by the indices() and outcomes() methods, and circuit is the unique circuit itself.

Returns

generator

copy()

Create a copy of this layout.

Returns

MatrixCOPALayout

resource_alloc(sub_alloc_name=None, empty_if_missing=True)

Retrieves the resource-allocation objectfor this layout.

Sub-resource-allocations can also be obtained by passing a non-None sub_alloc_name.

Parameters

sub_alloc_namestr

The name to retrieve

empty_if_missingbool

When True, an empty resource allocation object is returned when sub_alloc_name doesn’t exist for this layout. Otherwise a KeyError is raised when this occurs.

Returns

ResourceAllocation