classical_sampler(U, n, m)
classical_sampler(;input::Input, interf::Interferometer)

Sample photons according to the Distinguishable case.

classical_sampler(ev::Event{TIn, TOut}; occupancy_vector = true) where {TIn<:InputType, TOut <: FockSample}

Sampler for an Event. Note the difference of behaviour if occupancy_vector = true.

clifford_unoptimised(A, n; occupancy_vector = true)

Naive implementation of the Clifford algorithm. Computes a sample if n photons are sent in the first n modes of a m mode interferometer A.

cliffords_sampler(;input::Input, interf::Interferometer, nthreads=1)

Sample photons according to the Bosonic case following Clifford & Clifford algorithm performed (at most) in $O(n2^m + Poly(n,m))$ time and $O(m)$ space. The optional parameter nthreads is used to deploy the algorithm on several threads.

cliffords_sampler(ev::Event{TIn, TOut}; occupancy_vector = true) where {TIn<:InputType, TOut <: FockSample}

Sampler for an Event. Note the difference of behaviour if occupancy_vector = true.

gaussian_sampler(;input::GaussianInput{T}, nsamples=Int(1e3), burn_in=200, thinning_rate=100, mean_n=nothing) where {T<:Gaussian}

Simulate noiseless Gaussian Boson Sampling with photon number resolving detectors via a MIS sampler. Post-select the proposed states by fixing the mean photon number mean_n.

metropolis_sampler(;target_pdf, known_pdf, known_sampler, starting_state, n_iter, n_burn = 100, n_thinning = 100)

Implement a metropolis independent sampler for standard boson sampling following. The burn in period n_burnand the thinning interval n_thinning both have default value of 100.

noisy_sampler(;input::Input, loss::Real, interf::Interferometer)

Sample partially-distinguishable photons through a lossy interferometer, which runs (at most) in $O(n2^m + Poly(n,m))$ time.

noisy_sampler(ev::Event{TIn,TOut}, loss::Real; occupancy_vector=true) where {TIn<:InputType, TOut<:FockSample}

Noisy sampler for en Event.