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Paper IDQ.4.6
Paper Title Adaptive Procedures for Discriminating Between Arbitrary Tensor-Product Quantum States
Authors Sarah Anne Brandsen, Mengke Lian, Kevin Stubbs, Narayanan Rengaswamy, Henry Pfister, Duke University, United States
Session Q.4: Quantum Information Theory
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 Discrimination between quantum states is a fundamental task in quantum information theory. Given two arbitrary tensor-product quantum states (TPQS), implementing the optimal measurement on the full quantum system may be impractical. Thus, in this work we focus on identifying local measurement schemes that are near-optimal for distinguishing between two general TPQS. We begin by generalizing previous work to show that a locally greedy scheme using Bayesian updating can optimally distinguish between two pure TPQS states. Then, we show the same algorithm has poor performance in distinguishing mixed TPQS, and introduce a modified locally greedy scheme with strictly better performance. In the second part of this work, we compare these simple schemes with a more general dynamic programming (DP) approach that adaptively optimizes over measurement and subsystem ordering in each round.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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