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State and StateVector

class perceval.utils.statevector.BSDistribution(d=None)

Time-Independent probabilistic distribution of Basic States

group_modes_simplification(group_size)

Simplify this BSDistribution by grouping modes (ex: |1,3,0,0> -> |4,0> if group_size=2) These “coarse grain” simplification methods can be used to decrease the number of components of a given distribution.

Parameters:: group_size (int) – the size of the groups of modes
Return type:: BSDistribution
Returns:: the simplified distribution

static list_tensor_product(distributions, merge_modes=False, prob_threshold=0)

Return type:: BSDistribution

Efficient tensor product for a list of BasicState Distribution.: Can modify the distributions in place if merge_modes is False by adding empty modes. Performs len(distributions) - 1 tensor products

photon_threshold_simplification(photon_threshold)

Simplify this BSDistribution with a photon threshold for each mode (ex: |0,3,0,0> -> |0,1,0,0> if photon_threshold=1) These “coarse grain” simplification methods can be used to decrease the number of components of a given distribution.

Parameters:: photon_threshold (int) – the maximum number of photons per mode
Return type:: BSDistribution
Returns:: the simplified distribution

sample(count, non_null=True)

Samples basic states from the BSDistribution

Parameters:

count (int) – number of samples to draw
non_null (bool) – excludes null states from the sample generation

Return type:

BSSamples

Returns:

a list of \(count\) samples

static tensor_product(bsd1, bsd2, merge_modes=False, prob_threshold=0)

Compute the tensor product of two BasicState Distribution

Return type:: BSDistribution

class perceval.utils.statevector.ProbabilityDistribution: Time-Independent abstract probabilistic distribution of states

class perceval.utils.statevector.SVDistribution(sv=None)

Mixed state represented as a time-independent probabilistic distribution of StateVectors

static list_tensor_product(distributions, prob_threshold=0)

Efficient tensor product for a list of distributions

Return type:: SVDistribution

sample(count, non_null=True)

Generate a sample StateVector from the SVDistribution

Parameters:

non_null (bool) – excludes null states from the sample generation
count (int) – number of samples to draw

Return type:

list[StateVector]

Returns:

a list of \(count\) samples

static tensor_product(svd1, svd2, prob_threshold=0)

Compute the tensor product of two SVDistribution with an optional probability threshold

Return type:: SVDistribution

perceval.utils.statevector.allstate_iterator(input_state, mask=None)

Iterator on all possible output states compatible with mask generating StateVector

Parameters:

input_state (BasicState | StateVector) – a given input state vector
mask (xq.FSMask) – an optional mask

Return type:

BasicState

Returns:

list of output_state

perceval.utils.statevector.filter_distribution_photon_count(bsd, min_photons_filter)

Filter the states of a BSDistribution to keep only those having state.n >= min_photons_filter

Parameters:

bsd (BSDistribution) – the BSDistribution to filter out
min_photons_filter (int) – the minimum number of photons required to keep a state

Return type:

tuple[BSDistribution, float]

Returns:

a tuple containing the normalized filtered BSDistribution and the probability that the state is kept

perceval.utils.statevector.max_photon_state_iterator(m, n_max)

Iterator on all possible output state on m modes with at most n_max photons

Parameters:

m (int) – number of modes
n_max (int) – maximum number of photons

Returns:

list of BasicState

perceval.utils.statevector.tensorproduct(states)

Computes states[0] * states[1] * …

Return type:: StateVector | BasicState