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Integrated multimode interferometers with arbitrary designs for photonic boson sampling. Russell, Joshua W. Silverstone, Peter J. Marshall, Mark G.

Thompson, Jonathan C. Matthews, Toshikazu Hashimoto, Jeremy L. Universal linear optics. Science, —, August High-efficiency multiphoton boson sampling. Nature Photonics, 11 6 —, June You, Z. Wang, C. Schneider, Jelmer J. Physical Review Letters, 25 , December Quantum computational advantage using photons.

Science, —, December Birchall, Ashley Montanaro, and Anthony Laing. Classical boson sampling algorithms with superior performance to near-term experiments. Nature Physics, 13 12 —, December The Classical Complexity of Boson Sampling. Society for Industrial and Applied Mathematics, January Renema, Adrian Menssen, William R.

Clements, Gil Triginer, William S. Kolthammer, and Ian A. Physical Review Letters, 22 , May Classical simulability of noisy boson sampling.

Renema, and Valery Shchesnovich. Simulating boson sampling in lossy architectures. Quantum, , August Classical simulation of photonic linear optics with lost particles.

New Journal of Physics, 20 9 , September Publisher: IOP Publishing. Classical simulation of linear optics subject to nonuniform losses. Quantum, , May Renema, and Peter S. Classically simulating near-term partially-distinguishable and lossy boson sampling.

Quantum Science and Technology, 5 1 , November Distinguishing noisy boson sampling from classical simulations. Quantum, , March Speedup in classical simulation of Gaussian boson sampling.

Science Bulletin, 65 10 —, May Publisher: Elsevier B. Physical Review Letters, 10 , March Exact simulation of Gaussian boson sampling in polynomial space and exponential time. Physical Review Research, 2 2 , April Quadratic speedup for simulating Gaussian boson sampling. Simulability of partially distinguishable superposition and Gaussian boson sampling. Physical Review A, 6 , June On Crossref's cited-by service no data on citing works was found last attempt Copyright remains with the original copyright holders such as the authors or their institutions.

Skip to content. Quantum Advantage from Linear Optics Quantum computing has promised advantages in a wide variety of problems, including simulating physical systems, optimisation problems and machine learning [ 1 ].

Such imperfections include: loss, where a photon is generated at the start of the interferometer but not detected at the end and distinguishability, where photons are generated at different wavelengths, polarisations, or even at different times, and as a result no longer interfere with each other.

How to Spot the Difference So what can we do to ensure that we have an advantage over these classical algorithms? Conclusion and Outlook We have now reached the era in quantum computing where we are seeing the first claims of a quantum device performing a computation which is classically infeasible.

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