Neural networks for quantum inverse problems

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

Automated summary

In this paper, a neural-network-based method for solving the quantum inverse problem (QIP) is proposed. This method takes advantage of the quantumness of QIPs and utilizes the computational power of neural networks to achieve efficient quantum state estimation. The method is tested numerically and experimentally on the problem of maximum entropy estimation and demonstrates high fidelity, efficiency, and robustness.