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

TL;DR

This paper introduces a neural network-based approach to simulate and optimize multi-particle Gaussian states and boson sampling processes in quantum phase space, enhancing quantum technology design.

Contribution

It presents a novel neural network framework to model Gaussian states and optimize multi-particle events in boson sampling, enabling complex quantum process simulations.

Findings

Neural networks accurately model Gaussian state transformations.

Optimization of multi-particle events improves boson sampling probabilities.

Potential for designing advanced quantum sources and circuits.

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.