Technical Program

Paper Detail

Paper IDQ.3.6
Paper Title Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing
Authors Sarah Anne Brandsen, Kevin Stubbs, Henry Pfister, Duke University, United States
Session Q.3: Quantum Error Correcting Codes and Decoding
Presentation Lecture
Track Quantum Systems, Codes, and Information
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract Reinforcement learning with neural networks (RLNN) has demonstrated great promise for problems in quantum information theory. We apply RLNN to the problem of quantum hypothesis testing, where one tries to distinguish between multiple quantum states while minimizing the error probability. Finding the minimal error measurement is a challenging problem in general, and implementing the optimal measurement on the entire system may be impractical. In this work, we develop an experimentally feasible, locally adaptive measurement strategy for multiple hypothesis testing via RLNN. Numerical results for the network performance are shown. Finally, we introduce the concept of weak and strict t-locality and prove a partial ordering that allows us to simplify the total number of locally adaptive algorithms that need to be considered.

Plan Ahead


2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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