Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase space
This work addresses the challenge of simulating complex quantum systems for quantum technologies, but it appears incremental as it applies existing neural network methods to quantum phase space representations.
The researchers tackled the problem of simulating and optimizing multi-particle quantum processes by using neural networks to represent Gaussian states in quantum phase space, enabling the computation of boson pattern probabilities and optimization of Gaussian boson sampling events. They demonstrated this approach for non-classical light propagation in random interferometers, though no concrete numerical results were provided.
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.