Code for multilevel quantum gates now available on Github

We made available our Python and TensorFlow code about machine learning design of multilevel quantum gates with reservoir computing

GitHub Repository

See also

Phase space machine learning for multi-particle event optimization in Gaussian boson sampling

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.

Official code