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.


Mengke Lian

Duke University

Kevin Stubbs

Duke University

Henry Pfister

Duke University

Session Chair

Marco Tomamichel

National University of Singapore