Sampling algorithms¶
This submodule provides access to algorithms to sample from the hafnian or the torontonian of Gaussian quantum states.
Hafnian sampling¶

Returns a single sample from the Hafnian of a Gaussian state. 

Returns samples from the Hafnian of a Gaussian state. 

Returns samples from the Gaussian state specified by the adjacency matrix \(A\) and with total mean photon number \(n_{mean}\) 

Returns samples from a Gaussian state that has a positive \(P\) function. 

Returns samples from a rank one adjacency matrix bm{A} = bm{G} bm{G}^T where \(\bm{G}\) is a row vector. 
Torontonian sampling¶

Returns a single sample from the Hafnian of a Gaussian state. 

Returns samples from the Torontonian of a Gaussian state 

Returns samples from the Torontonian of a Gaussian state specified by the adjacency matrix \(A\) and with total mean photon number \(n_{mean}\) 

Returns threshold samples from a Gaussian state that has a positive P function. 
Brute force sampling¶

Given a photonnumber probability mass function(PMF) it returns samples according to said PMF. 
Code details¶

generate_hafnian_sample
(cov, mean=None, hbar=2, cutoff=6, max_photons=30, approx=False, approx_samples=100000.0)[source]¶ Returns a single sample from the Hafnian of a Gaussian state.
 Parameters
cov (array) – a \(2N\times 2N\)
np.float64
covariance matrix representing an \(N\) mode quantum state. This can be obtained via thescovmavxp
method of the Gaussian backend of Strawberry Fields.mean (array) – a \(2N\)
np.float64
vector of means representing the Gaussian state.hbar (float) – (default 2) the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\).
cutoff (int) – the Fock basis truncation.
max_photons (int) – specifies the maximum number of photons that can be counted.
approx (bool) – if
True
, the approximate hafnian algorithm is used. Note that this can only be used for real, nonnegative matrices.approx_samples – the number of samples used to approximate the hafnian if
approx=True
.
 Returns
a photon number sample from the Gaussian states.
 Return type
np.array[int]

generate_torontonian_sample
(cov, hbar=2, max_photons=30)[source]¶ Returns a single sample from the Hafnian of a Gaussian state.
 Parameters
cov (array) – a \(2N\times 2N\)
np.float64
covariance matrix representing an \(N\) mode quantum state. This can be obtained via thescovmavxp
method of the Gaussian backend of Strawberry Fields.hbar (float) – (default 2) the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\).
max_photons (int) – specifies the maximum number of clicks that can be counted.
 Returns
a threshold sample from the Gaussian state.
 Return type
np.array[int]

hafnian_sample_classical_state
(cov, samples, mean=None, hbar=2, atol=1e08, cutoff=None)[source]¶ Returns samples from a Gaussian state that has a positive \(P\) function.
 Parameters
cov (array) – a \(2N\times 2N\)
np.float64
covariance matrix representing an \(N\) mode quantum state. This can be obtained via thescovmavxp
method of the Gaussian backend of Strawberry Fields.samples (int) – number of samples to generate
mean (array) – vector of means of the gaussian state
hbar (float) – the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\).
sigdigits (integer) – precision to check that the covariance matrix is a true covariance matrix of a gaussian state.
 Returns
photon number samples from the Gaussian state with covariance cov and vector means mean.
 Return type
np.array[int]

hafnian_sample_graph
(A, n_mean, samples=1, cutoff=5, max_photons=30, approx=False, approx_samples=100000.0, parallel=False)[source]¶ Returns samples from the Gaussian state specified by the adjacency matrix \(A\) and with total mean photon number \(n_{mean}\)
 Parameters
A (array) – a \(N\times N\)
np.float64
(symmetric) adjacency matrix matrixn_mean (float) – mean photon number of the Gaussian state
samples (int) – the number of samples to return.
cutoff (int) – the Fock basis truncation.
max_photons (int) – specifies the maximum number of photons that can be counted.
approx (bool) – if
True
, the approximate hafnian algorithm is used. Note that this can only be used for real, nonnegative matrices.approx_samples – the number of samples used to approximate the hafnian if
approx=True
.parallel (bool) – if
True
, usesdask
for parallelization of samples
 Returns
photon number samples from the Gaussian state
 Return type
np.array[int]

hafnian_sample_graph_rank_one
(G, n_mean, samples=1)[source]¶ Returns samples from a rank one adjacency matrix bm{A} = bm{G} bm{G}^T where \(\bm{G}\) is a row vector.
 Parameters
G (array) – factorization of the rankone matrix A = G @ G.T.
nmean (float) – Total mean photon number.
samples (int) – the number of samples to return.
 Returns
(array): samples.

hafnian_sample_state
(cov, samples, mean=None, hbar=2, cutoff=5, max_photons=30, approx=False, approx_samples=100000.0, parallel=False)[source]¶ Returns samples from the Hafnian of a Gaussian state.
 Parameters
cov (array) – a \(2N\times 2N\)
np.float64
covariance matrix representing an \(N\) mode quantum state. This can be obtained via thescovmavxp
method of the Gaussian backend of Strawberry Fields.samples (int) – the number of samples to return.
mean (array) – a \(2N\)
np.float64
vector of means representing the Gaussian state.hbar (float) – (default 2) the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\).
cutoff (int) – the Fock basis truncation.
max_photons (int) – specifies the maximum number of photons that can be counted.
approx (bool) – if
True
, thehafnian_approx()
function is used to approximate the hafnian. Note that this can only be used for real, nonnegative matrices.approx_samples – the number of samples used to approximate the hafnian if
approx=True
.parallel (bool) – if
True
, usesdask
for parallelization of samples
 Returns
photon number samples from the Gaussian state
 Return type
np.array[int]

photon_number_sampler
(probabilities, num_samples, out_of_bounds=False)[source]¶ Given a photonnumber probability mass function(PMF) it returns samples according to said PMF.
 Parameters
probabilities (array) – probability tensor of the modes, has shape
[cutoff]*num_modes
num_samples (int) – number of samples requested
out_of_bounds (boolean) – if
False
the probability distribution is renormalized. If notFalse
, the value ofout_of_bounds
is used as a placeholder for samples where more than the cutoff of probabilities are detected.
 Returns
Samples, with shape [num_sample, num_modes]
 Return type
(array)

seed
(seed_val=None)[source]¶ Seeds the random number generator used in the sampling algorithms.
This function is a wrapper around
numpy.random.seed()
. By setting the seed to a specific integer, the sampling algorithms will exhibit deterministic behaviour. Parameters
seed_val (int) – Seed for RandomState. Must be convertible to 32 bit unsigned integers.

torontonian_sample_classical_state
(cov, samples, mean=None, hbar=2, atol=1e08)[source]¶ Returns threshold samples from a Gaussian state that has a positive P function.
 Parameters
cov (array) – a \(2N\times 2N\)
np.float64
covariance matrix representing an \(N\) mode quantum state. This can be obtained via thescovmavxp
method of the Gaussian backend of Strawberry Fields.samples (int) – number of samples to generate
mean (array) – vector of means of the Gaussian state
hbar (float) – the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\).
sigdigits (integer) – precision to check that the covariance matrix is a true covariance matrix of a gaussian state.
 Returns
threshold samples from the Gaussian state with covariance cov and vector means mean.
 Return type
np.array[int]

torontonian_sample_graph
(A, n_mean, samples=1, max_photons=30, parallel=False)[source]¶ Returns samples from the Torontonian of a Gaussian state specified by the adjacency matrix \(A\) and with total mean photon number \(n_{mean}\)
 Parameters
A (array) – a \(N\times N\)
np.float64
(symmetric) adjacency matrix matrixn_mean (float) – mean photon number of the Gaussian state
samples (int) – the number of samples to return.
max_photons (int) – specifies the maximum number of photons that can be counted.
parallel (bool) – if
True
, usesdask
for parallelization of samples
 Returns
photon number samples from the Torontonian of the Gaussian state
 Return type
np.array[int]

torontonian_sample_state
(cov, samples, hbar=2, max_photons=30, parallel=False)[source]¶ Returns samples from the Torontonian of a Gaussian state
 Parameters
cov (array) – a \(2N\times 2N\)
np.float64
covariance matrix representing an \(N\) mode quantum state. This can be obtained via thescovmavxp
method of the Gaussian backend of Strawberry Fields.samples (int) – number of samples to generate
hbar (float) – (default 2) the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\).
max_photons (int) – specifies the maximum number of clicks that can be counted.
parallel (bool) – if
True
, usesdask
for parallelization of samples
 Returns
threshold samples from the Gaussian state.
 Return type
np.array[int]
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