Quantum machine learning and Boson sampling

Training Gaussian boson sampling by quantum machine learning

published in Quantum Machine Intelligence 3, 26 (2021)

Pseudocode

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.

https://doi.org/10.1007/s42484-021-00052-y

Our Replica Symmetry Breaking experiments in the motivations of the Nobel Prize to Giorgio Parisi !!!

The main goal of the Light and Complexity project (ERC StG 2007) was to observe replica symmetry breaking (a process predicted by Giorgio Parisi) in random lasers and nonlinear waves.

After our successful experiments in 2015 and 2017, the results are now cited in the motivations of the 2021 Nobel Prize to Giorgio Parisi !

https://www.nobelprize.org/uploads/2021/10/sciback_fy_en_21.pdf

See also