pygsti.layouts.maplayout

Defines the MapCOPALayout class.

Module Contents

Classes

_MapCOPALayoutAtom

The atom ("atomic unit") for dividing up the element dimension in a MapCOPALayout.

MapCOPALayout

A circuit outcome probability array (COPA) layout for circuit simulation by state vector maps.

class pygsti.layouts.maplayout._MapCOPALayoutAtom(unique_complete_circuits, ds_circuits, group, model, dataset, max_cache_size)

Bases: pygsti.layouts.distlayout._DistributableAtom

The atom (“atomic unit”) for dividing up the element dimension in a MapCOPALayout.

Parameters
  • unique_complete_circuits (list) – A list that contains all the “complete” circuits for the parent layout. This atom only owns a subset of these, as given by group below.

  • ds_circuits (list) – A parallel list of circuits as they should be accessed from dataset. This applies any aliases and removes implied SPAM elements relative to unique_complete_circuits.

  • group (set) – The set of unique-circuit indices (i.e. indices into unique_complete_circuits) that this atom owns.

  • model (Model) – The model being used to construct this layout. Used for expanding instruments within the circuits.

  • dataset (DataSet) – The dataset, used to include only observed circuit outcomes in this atom and therefore the parent layout.

  • max_cache_size (int) – The maximum allowed cache size, given as number of quantum states.

_update_indices(self, old_unique_is_by_new_unique_is)

Updates any internal indices held as a result of the unique-circuit indices of the layout changing.

This function is called during layout construction to alert the atom that the layout being created will only hold a subset of the unique_complete_circuits provided to to the atom’s __init__ method. Thus, if the atom keeps indices to unique circuits within the layout, it should update these indices accordingly.

Parameters

old_unique_is_by_new_unique_is (list) – The indices within the unique_complete_circuits given to __init__ that index the unique circuits of the created layout - thus, these that will become (in order) all of the unique circuits of the created layout.

Returns

None

property cache_size(self)

The cache size of this atom.

class pygsti.layouts.maplayout.MapCOPALayout(circuits, model, dataset=None, max_cache_size=None, num_sub_tables=None, num_table_processors=1, num_param_dimension_processors=(), param_dimensions=(), param_dimension_blk_sizes=(), resource_alloc=None, verbosity=0)

Bases: pygsti.layouts.distlayout.DistributableCOPALayout

A circuit outcome probability array (COPA) layout for circuit simulation by state vector maps.

A simple distributed layout that divides a list of circuits among available processors and optionally supports caching “prefix” states that result from common prefixes found in the circuits.

Parameters
  • circuits (list) – A list of:class:Circuit objects representing the circuits this layout will include.

  • model (Model) – The model that will be used to compute circuit outcome probabilities using this layout. This model is used to complete and expand the circuits in circuits.

  • dataset (DataSet, optional) – If not None, restrict the circuit outcomes stored by this layout to only the outcomes observed in this data set.

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

  • num_sub_tables (int, optional) – The number of groups (“sub-tables”) to divide the circuits into. This is the number of atoms for this layout.

  • num_table_processors (int, optional) – The number of atom-processors, i.e. groups of processors that process sub-tables.

  • num_param_dimension_processors (tuple, optional) – A 1- or 2-tuple of integers specifying how many parameter-block processors are used when dividing the physical processors into a grid. The first and second elements correspond to counts for the first and second parameter dimensions, respecively.

  • param_dimensions (tuple, optional) – The number of parameters along each parameter dimension. Can be an empty, 1-, or 2-tuple of integers which dictates how many parameter dimensions this layout supports.

  • param_dimension_blk_sizes (tuple, optional) – The parameter block sizes along each present parameter dimension, so this should be the same shape as param_dimensions. 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.

  • resource_alloc (ResourceAllocation, optional) – The resources available for computing circuit outcome probabilities.

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