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arXiv:2609.28440

Sharp pairwise reduction for quantum hypothesis testing

Kuan-Yi Lee, Ludovico Lami

quant-phSubmitted Sep 23, 2026• Announced Sep 24, 2026• 0 citations
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Abstract

We determine the optimal universal coefficient in a reduction of multiple to binary quantum hypothesis testing. Specifically, for every finite ensemble in any Hilbert space dimension (finite or infinite), we prove that the error probability of the global pretty good measurement (PGM) is at most four times the sum of the optimal binary error probabilities. By constructing a family of regular-simplex ensembles, we further show that the coefficient four is optimal, even for arbitrary global measurements. This result improves on the pairwise bounds established by Cheng and Liu [arXiv:2606.06246 (2026)] and entails an explicit, sharp guarantee for the standard PGM itself. Our proof is also simpler: rather than constructing sequential measurements and applying a union bound, our proof relies on purely matrix-analytic techniques, combining a direct block Gram matrix analysis of the PGM error and a refined inequality between quantum Hellinger distance and Bures $χ^2$-divergence. Our analysis also yields a refined, one-shot pairwise Chernoff bound and explicit sufficient number of copies for a desired discrimination error.

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11 pages

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We determine the optimal universal coefficient in a reduction of multiple to binary quantum hypothesis testing. Specifically, for every finite ensemble in any Hilbert space dimension (finite or infinite), we prove that the error probability of the global pretty good measurement (PGM) is at most four times the sum of the optimal binary error probabilities. By constructing a family of regular-simplex ensembles, we further show that the coefficient four is optimal, even for arbitrary global measurements. This result improves on the pairwise bounds established by Cheng and Liu [arXiv:2606.06246 (2026)] and entails an explicit, sharp guarantee for the standard PGM itself. Our proof is also simpler: rather than constructing sequential measurements and applying a union bound, our proof relies on purely matrix-analytic techniques, combining a direct block Gram matrix analysis of the PGM error and a refined inequality between quantum Hellinger distance and Bures χ2χ^2χ2-divergence. Our analysis also yields a refined, one-shot pairwise Chernoff bound and explicit sufficient number of copies for a desired discrimination error.
We consider Bayesian discrimination among multiple quantum states and establish a dimension-free one-shot upper bound on the minimum probability of error in terms of the sum of pairwise errors. This resolves a conjecture of Audenaert and Mosonyi [J. Math. Phys. 55 (2014)] and improves the multiple quantum Chernoff bound of Li [Ann. Statist. 44 (2016)] by removing its dimension-dependent prefactor. In the asymptotic many-copy regime, our bound proves the achievability of the multiple quantum Chernoff distance for arbitrary separable Hilbert spaces, thereby settling the previously open infinite-dimensional case, and further yields constant-factor sharp asymptotics for the optimal error probability. In binary quantum hypothesis testing, we prove that the minimum error probability is characterized, up to universal constants, by a trace harmonic-mean quantity. Consequently, the optimal binary quantum error probability is within a factor of two of the optimal classical error probability for the associated Nussbaum-Szkoła distributions, complementing the lower bound of Nussbaum and Szkoła [Ann. Statist. 37 (2009)].
We derive a new lower bound on the success probability of the Pretty Good Measurement (PGM) for worst-case quantum state discrimination among mmm pure states. Our bound is strictly tighter than the previously known Gram-matrix-based bound for m≥4m\geq 4m≥4. The proof adapts techniques from Barnum and Knill's analysis of the average-case PGM, applied here to the worst-case scenario. By comparing the PGM to the sequential measurement algorithm, we obtain a guarantee showing that, in the low-fidelity regime, the PGM's success probability decreases quadratically with respect to the maximum pairwise overlap, rather than linearly as in earlier bounds.
We show that the quantum measurement known as the pretty good measurement can be used to identify an unknown quantum state picked from any set of nnn mixed states that have pairwise fidelities upper-bounded by a constant below 1, given O(log⁡n)O(\log n)O(logn) copies of the unknown state, with high success probability in the worst case. If the unknown state is promised to be pure, there is an explicit measurement strategy which solves this worst-case quantum state discrimination problem with O~(∥G∥)\widetilde{O}(\|G\|)O(∥G∥) copies, where GGG is the Gram matrix of the states.
Quantum state ensembles are important in quantum information processing. For example, quantum ttt-designs model highly entangled states in complex systems, while projected ensembles appear in generative quantum machine learning and studies of thermalization. With their sample state accompanied by a classical label, these ensembles contain operational information beyond their average density operators. Yet an ensemble differs from a classical-quantum state because it is invariant under permutations of labels. We formulate binary hypothesis testing between finite quantum ensembles and derive fundamental limits on error probability. Given an observed label pattern, we show that the joint sampled state can be described by power-weighted ensemble moments. This yields the Bayes-optimal measurement and exact finite-sample error, revealing that discrimination is governed by the full moment hierarchy up to the number of samples. In the many-sample limit, we derive Chernoff bounds and obtain exact error exponents for finite uniform pure-state ensembles. We apply these results to optical communication and ttt-designs. For finite uniform pure-state ttt-designs with large ttt, the maximal discrimination exponent scales sharply as ∼t−2\sim t^{-2}∼t−2, while equal-prior fixed-error testing requires ∼t2\sim t^2∼t2 samples.
We consider the task of distinguishing whether a quantum system is prepared in a state from one of several sets of quantum states. Assuming their convexity and stability under tensor product, we prove that the optimal error exponent for discrimination is precisely given by the regularized quantum Chernoff divergence between the sets, thereby establishing a generalized quantum Chernoff bound for the discrimination of multiple sets of quantum states. This extends the classical and quantum Chernoff bounds to the general setting of composite and correlated quantum hypotheses. Furthermore, leveraging minimax theorems, we show that discriminating between sets of quantum states is no harder than discriminating between their worst-case elements in terms of error probability. This implies the existence of an optimal state-agnostic test that achieves the minimum error probability for all states in the sets, matching the performance of the optimal state-dependent test for the most difficult pair of states. We provide explicit characterizations of the optimal state-agnostic test in the binary composite case. Finally, we show that the maximum overlap between a pure state and a set of free states, a quantity that frequently arises in quantum resource theories, is equal to the quantum Chernoff divergence between the sets, thereby providing an operational interpretation of this quantity in the context of symmetric hypothesis testing.
Quantum communication requires a receiver to distinguish several, generally mixed, signal states. A useful theoretical question is whether errors on smaller signal alphabets can certify the error of a decoder for the full alphabet. We study this question for the pretty good measurement and present it as a use case for the programme of extracting reusable mathematical instruments from a large collection of research manuscripts. A constrained spectral optimization mechanism identified in that collection leads to the sharp pairwise coefficient (1 + sqrt(1 - 2/N))^2 for all equally likely pure ensembles. A separate matrix analysis proves the sharp coefficient (1 + sqrt(1 - k/N))^2 for every subset size when the average state is maximally mixed on its support, allowing arbitrary mixed signals. For general mixed ensembles, the sharp leave-one coefficient (1 + 1/sqrt(N))^2 holds whenever the normalized full error is at least 1/N; consequently it holds for all equally likely ensembles in dimension at most N - 1. We give a noisy qutrit decoding example, an unrestricted bound for nearly full subsets, and an exact polynomial certificate for a further four-signal family. The account separates the extracted mechanisms, their classical antecedents, and the additional arguments needed for the quantum problem. These results provide conditional error certificates, while the unrestricted sharp mixed-state subset problem and improvements to operational communication rates remain open.