Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Conference Paper

Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply reinforcement learning to quantum hypothesis testing, where one designs measurements that can distinguish between multiple quantum states j = 1 while minimizing the error probability. Although the Helstrom measurement is known to be optimal when there are m=2 states, the general problem of finding a minimal-error measurement is challenging. Additionally, in the case where the candidate states correspond to a quantum system with many qubit subsystems, implementing the optimal measurement on the entire system may be impractical. In this work, we develop locally-adaptive measurement strategies that are experimentally feasible in the sense that only one quantum subsystem is measured in each round. RLNN is used to find the optimal measurement protocol for arbitrary sets of tensor product quantum states. Numerical results for the network performance are shown. In special cases, the neural network testing-policy achieves the same probability of success as the optimal collective measurement.

Full Text

Duke Authors

Cited Authors

  • Brandsen, S; Stubbs, KD; Pfister, HD

Published Date

  • June 1, 2020

Published In

Volume / Issue

  • 2020-June /

Start / End Page

  • 1897 - 1902

International Standard Serial Number (ISSN)

  • 2157-8095

International Standard Book Number 13 (ISBN-13)

  • 9781728164328

Digital Object Identifier (DOI)

  • 10.1109/ISIT44484.2020.9174150

Citation Source

  • Scopus