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arXiv · 2307.16347

Minimum-consumption discrimination of quantum states via globally optimal adaptive measurements

Boxuan Tian·Wenzhe Yan·Zhibo Hou·Guo-Yong Xiang·Chuan-Feng Li·Guang-Can Guo
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Abstract

Reducing the average resource consumption is the central quest in discriminating non-orthogonal quantum states for a fixed admissible error rate $\varepsilon$. The globally optimal fixed local projective measurement (GOFL) for this task is found to be different from that for previous minimum-error discrimination tasks [PRL 118, 030502 (2017)]. To achieve the ultimate minimum average consumption, here we develop a general globally optimal adaptive strategy (GOA) by subtly using the updated posterior probability, which works under any error rate requirement and any one-way measurement restrictions, and can be solved by a convergent iterative relation. First, under the local measurement restrictions, our GOA is solved to serve as the local bound, which saves 16.6 copies (24%) compared with the previously best GOFL. When the more powerful two-copy collective measurements are allowed, our GOA is experimentally demonstrated to beat the local bound by 3.9 copies (6.0%). By exploiting both adaptivity and collective measurements, our work marks an important step towards minimum-consumption quantum state discrimination.

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Boxuan Tian, Wenzhe Yan, Zhibo Hou, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo. 2023-07-30. Minimum-consumption discrimination of quantum states via globally optimal adaptive measurements. https://arxiv.org/abs/2307.16347

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