Neural networks for quantum inverse problems

Abstract Quantum inverse problem (QIP) is the of estimating an unknown quantum system from a set measurements, whereas classical counterpart distribution observations. In this paper, we present neural-network-based method for QIPs, which has been widely explored its counterpart. The proposed utilizes quantumness QIPs and takes advantage computational power neural networks to achieve remarkable efficiency state estimation. We test on maximum entropy estimation ? partial information both numerically experimentally. Our yields high fidelity, robustness numerical experiments optical experiments.