A new algorithm converts low-entanglement bosonic Gaussian states to matrix product states in polynomial time without hafnian calculations, yielding speedups on experimental boson sampling data.
On classi- cal simulation algorithms for noisy boson sampling
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Presents a tensor network method in Heisenberg picture for computing permanents in Boson Sampling at optimal classical complexity with extensions to imperfections.
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Efficient simulation of low-entanglement bosonic Gaussian states in polynomial time
A new algorithm converts low-entanglement bosonic Gaussian states to matrix product states in polynomial time without hafnian calculations, yielding speedups on experimental boson sampling data.
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Heisenberg picture tensor network formalism for optical circuits
Presents a tensor network method in Heisenberg picture for computing permanents in Boson Sampling at optimal classical complexity with extensions to imperfections.