Source

class exqalibur.Source

Definition of a source We build on a phenomenological model first introduced in ref. [1] where an imperfect quantum-dot based single-photon source is modeled by a statistical mixture of Fock states. The model developed here, first introduced in ref. [2], constructs the input multi-photon state using features specific to Perceval.

[1] Pont, Mathias, et al. Physical Review X 12, 031033 (2022). https://doi.org/10.1103/PhysRevX.12.031033

[2] Pont, Mathias, et al. arXiv preprint arXiv:2211.15626 (2022). https://doi.org/10.48550/arXiv.2211.15626

Parameters:

  • emission_probability – probability that the source emits at least one photon

  • multiphoton_component – second order intensity autocorrelation at zero time delay \(g^{(2)}(0)\)

  • indistinguishability – 2-photon mean wavepacket overlap

  • losses – optical losses

  • g2_distinguishable – True if additional photons are distinguishable, False otherwise

create_sampler(self: exqalibur.Source, expected_input: exqalibur.FockState, min_photons_filter: SupportsInt = 0) → exqalibur.SourceSampler

Creates a SourceSampler that will be able to sample states according to this source input probability distribution corresponding to the given expected input state without needing to generate it.

Parameters:

  • expected_input – Expected input BasicState Sampler will try to generate photons according to the source where photons of this state are.

  • min_photons_filter – Minimum number of photons of the states to generate.

Returns:

A SourceSampler corresponding to this source

create_simple_iterator(self: exqalibur.Source, expected_input: exqalibur.FockState, min_photons_filter: SupportsInt = 0) → exqalibur.SimpleSourceIterator
generate_distribution(self: exqalibur.Source, expected_input: exqalibur.FockState, prob_threshold: SupportsFloat = 0.0) → exqalibur.SVDistribution

Simulates plugging the photonic source on certain modes and turning it on. Computes the input probability distribution

Parameters:

  • expected_input – Expected input BasicState The properties of the source will alter the input state. A perfect source always delivers the expected state as an input. Imperfect ones won’t.

  • prob_threshold – Probability threshold under which the resulting state is filtered out.

property is_perfect
Returns:

True if the source is perfect (the distribution consists of the expected state with probability one), False otherwise

property partially_distinguishable
Returns:

True if the source needs to use NoisyStates to describe the distribution, False otherwise (only FockStates)

SourceSampler

class exqalibur.SourceSampler

The Sampler class is dedicated to sample according to a source distribution by using a simplified, source-specific structure, making it lighter and faster than generating the distribution.

The Sampler must be created using the Source.create_sampler method.

>>> import exqalibur as xq
>>> source = xq.Source(0.9, 0.03, 0.8)
>>> sampler = source.create_sampler(xq.FockState([1, 0, 1]), 1)
>>> [print(sample, end = ", ") for sample in sampler.generate_samples(5)]
|{_:0},0,{_:0}>, |0,0,{_:0}>, |{_:0},0,{_:0}>, |{_:0},0,{_:1}>, |{_:0}{_:1},0,{_:0}>,
property expected_input
Returns:

The target state that will be sampled from.

generate_samples(self: exqalibur.SourceSampler, n_samples: SupportsInt) → list[exqalibur.NoisyFockState]

Generates n_samples NoisyStates according to the source distribution for this sampler expected input.

Parameters:

n_samples – the number of states to generate

Returns:

a list of n_samples NoisyStates

generate_separated_samples(self: exqalibur.SourceSampler, n_samples: SupportsInt) → list[exqalibur.BSSamples]

Generates input states from a noisy mixed state, each one corresponding to the result of NoisyFockState.separate_state as generated according to the source distribution for this sampler expected input.

Uses a simplified procedure to be faster than calling generate_samples and separate_state.

Parameters:

n_samples – the number of separated states to generate

Returns:

a list of n_samples BSSamples

property min_photons_filter
Returns:

The minimum number of wanted photons. States having less than this number of photons will not be generated.

property physical_perf
Returns:

The probability of states with less than self.min_detected_photons photons in the original distribution.

property zpp
Returns:

The probability of the 0-photon state in the original distribution.