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

arXiv: https://arxiv.org/abs/2102.12142 · HTML: https://mlsanity.com/paper/2102.12142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)
- [Wave Physics as an Analog Recurrent Neural Network](https://mlsanity.com/paper/1904.12831/) (2019)
- [Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons](https://mlsanity.com/paper/1912.07044/) (2019)
- [Experimental Phase Estimation Enhanced By Machine Learning](https://mlsanity.com/paper/1712.07570/) (2017)
- [Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow](https://mlsanity.com/paper/1905.05264/) (2019)
- [Benchmarking 50-Photon Gaussian Boson Sampling on the Sunway TaihuLight](https://mlsanity.com/paper/2009.01177/) (2020)

## Built on by (most cited first, 1 of 1)

- [Variational quantum algorithm for Gaussian discrete solitons and their boson sampling](https://mlsanity.com/paper/2110.12379/) (2021)

## Abstract (arXiv)

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.