The effect of partial post-selection on quantum discrimination

• URL: http://arxiv.org/abs/2506.14105v2
• Date: Wed, 09 Jul 2025 19:44:56 GMT
• Title: The effect of partial post-selection on quantum discrimination
• Authors: Qipeng Qian, Christos N. Gagatsos,
• Abstract summary: We show that prior state engineering via partial measurement does not improve the average discrimination performance.<n>We also identify specific post-selection outcomes under which the conditional discrimination can achieve strictly lower error probabilities than the original optimal measurement.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The discrimination of quantum states is a central problem in quantum information science and technology. Meanwhile, partial post-selection has emerged as a valuable tool for quantum state engineering. In this work, we bring these two areas together and ask whether partial measurements can enhance the discrimination performance between two unknown and non-orthogonal pure states. Our framework is general: the two unknown states interact with the same environment-set in a pure state-via an arbitrary unitary transformation. A partial measurement is then performed on one of the output modes, modeled by an arbitrary positive operator-valued measure (POVM). We then allow classical communication to inform the unmeasured mode of the outcome of the partial measurement on the other mode, which is subsequently measured by a POVM that is optimal in the sense that the probability of error is minimized. The two POVMs act locally, but since we allow for classical communication between the two modes, we consider a scheme involving local operations and classical communication (LOCC). Under these considerations, we show that the minimum error probability, averaged over all possible post-selected branches, cannot be reduced below the minimum error probability of discriminating the original input states. In other words, prior state engineering via partial measurement does not improve the average discrimination performance. However, we also identify specific post-selection outcomes under which the conditional discrimination can achieve strictly lower error probabilities than the original optimal measurement, illustrating that while post-selection does not improve average performance, it can enable better discrimination in certain branches of the post-selected ensemble.

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