Training Gaussian boson sampling by quantum machine learning

Claudio Conti

doi:10.1007/s42484-021-00052-y

Training Gaussian boson sampling by quantum machine learning

2021-10-13

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Claudio Conti *

DOI：10.1007/s42484-021-00052-y

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Abstract

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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. This is a viable strategy for training Gaussian boson sampling. We 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.

Keywords:

Machine learning

Gaussian Boson sampling

Training Gaussian boson sampling by quantum machine learning

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