Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase space

Abstract

We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to simulate complex multi-particle processes. We use the layered neural networks for non-classical light propagation in random interferometers, and compute boson pattern probabilities by automatic differentiation. We also demonstrate that multi-particle events in Gaussian boson sampling can be optimized by a proper design and training of the neural network weights. The results are potentially useful to the creation of new sources and complex circuits for quantum technologies.

Extended version, with correct figure 4, code available in github

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Cited by in corpus (1)

• Variational quantum algorithm for Gaussian discrete solitons and their boson sampling