Please use this identifier to cite or link to this item: https://doi.org/10.22331/Q-2020-09-11-320
Title: Worst-case quantum hypothesis testing with separable measurements
Authors: Thinh, L.P.
Dall’Arno, M. 
Scarani, V. 
Issue Date: 2020
Publisher: Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
Citation: Thinh, L.P., Dall’Arno, M., Scarani, V. (2020). Worst-case quantum hypothesis testing with separable measurements. Quantum 4. ScholarBank@NUS Repository. https://doi.org/10.22331/Q-2020-09-11-320
Rights: Attribution 4.0 International
Abstract: For any pair of quantum states (the hypotheses), the task of binary quantum hypotheses testing is to derive the tradeoff relation between the probability p01 of rejecting the null hypothesis and p10 of accepting the alternative hypothesis. The case when both hypotheses are explicitly given was solved in the pioneering work by Helstrom. Here, instead, for any given null hypothesis as a pure state, we consider the worst-case alternative hypothesis that maximizes p10 under a constraint on the distinguishability of such hypotheses. Additionally, we restrict the optimization to separable measurements, in order to describe tests that are performed locally. The case p01 = 0 has been recently studied under the name of “quantum state verification”. We show that the problem can be cast as a semi-definite program (SDP). Then we study in detail the two-qubit case. A comprehensive study in parameter space is done by solving the SDP numerically. We also obtain analytical solutions in the case of commuting hypotheses, and in the case where the two hypotheses can be orthogonal (in the latter case, we prove that the restriction to separable measurements generically prevents perfect distinguishability). In regards to quantum state verification, our work shows the existence of more efficient strategies for noisy measurement scenarios. © 2020 Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften.
Source Title: Quantum
URI: https://scholarbank.nus.edu.sg/handle/10635/197484
ISSN: 2521327X
DOI: 10.22331/Q-2020-09-11-320
Rights: Attribution 4.0 International
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