Energy storage and quantum simulations with optical networks

Abstract

Quantum optical neural networks (QONN) stand out as promising variational algorithms for quantum simulations by leveraging the potential advantages of photonic circuits with respect to other hardware platforms. In this work, the basics of our proposed physically-inspired architecture of a continuousvariable QONN and its applicability to both optimization of quantum energy storage devices and simulation of quantum field theory will be discussed. Concerning the former, the dependence of the relative energy precision on stellar rank and bipartite correlations in quantum battery systems will be analysed, whereas for the latter, a preliminary study of the Orbifold formulation of U(1) Yang-Mills theory through exact diagonalisation will be presented.