2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | Q.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. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
