English

Optimal Discrimination Between Two Pure States and Dolinar-Type Coherent-State Detection

Quantum Physics 2023-11-07 v1 Information Theory math.IT

Abstract

We consider the problem of discrimination between two pure quantum states. It is well known that the optimal measurement under both the error-probability and log-loss criteria is a projection, while under an ``erasure-distortion'' criterion it is a three-outcome positive operator-valued measure (POVM). These results were derived separately. We present a unified approach which finds the optimal measurement under any distortion measure that satisfies a convexity relation with respect to the Bhattacharyya distance. Namely, whenever the measure is relatively convex (resp. concave), the measurement is the projection (resp. three-outcome POVM) above. The three above-mentioned results are obtained as special cases of this simple derivation. As for further measures for which our result applies, we prove that Renyi entropies of order 11 and above (resp. 1/21/2 and below) are relatively convex (resp. concave). A special setting of great practical interest, is the discrimination between two coherent-light waveforms. In a remarkable work by Dolinar it was shown that a simple detector consisting of a photon counter and a feedback-controlled local oscillator obtains the quantum-optimal error probability. Later it was shown that the same detector (with the same local signal) is also optimal in the log-loss sense. By applying a similar convexity approach, we obtain in a unified manner the optimal signal for a variety of criteria.

Keywords

Cite

@article{arxiv.2311.02366,
  title  = {Optimal Discrimination Between Two Pure States and Dolinar-Type Coherent-State Detection},
  author = {Itamar Katz and Alex Samorodnitsky and Yuval Kochman},
  journal= {arXiv preprint arXiv:2311.02366},
  year   = {2023}
}

Comments

Accepted to IEEE Trans. Info. Theory. Presented in part in ITW 2020, virtual, Apr. 2021, and in the 58th Allerton conference, Monticello, IL, Sep. 2022

R2 v1 2026-06-28T13:11:30.166Z