Adversarial hypothesis testing and a quantum Stein's Lemma for restricted measurements
Fernando G.S.L. Brandao, Aram W. Harrow, James R. Lee, Yuval, Peres

TL;DR
This paper extends classical hypothesis testing to an adaptive adversarial setting, demonstrating that simple maximum-likelihood tests remain optimal and applying this to quantum state discrimination with restricted measurements, including a quantum Stein's Lemma.
Contribution
It introduces an adaptive adversarial framework for hypothesis testing, showing the optimality of maximum-likelihood tests and deriving a quantum Stein's Lemma for restricted measurements.
Findings
Optimal error exponents are achievable by simple tests even under adversarial adaptation.
The framework applies to quantum state discrimination with LOCC measurements.
Provides an alternative proof of strong subadditivity for quantum relative entropy.
Abstract
Recall the classical hypothesis testing setting with two convex sets of probability distributions P and Q. One receives either n i.i.d. samples from a distribution p in P or from a distribution q in Q and wants to decide from which set the points were sampled. It is known that the optimal exponential rate at which errors decrease can be achieved by a simple maximum-likelihood ratio test which does not depend on p or q, but only on the sets P and Q. We consider an adaptive generalization of this model where the choice of p in P and q in Q can change in each sample in some way that depends arbitrarily on the previous samples. In other words, in the k'th round, an adversary, having observed all the previous samples in rounds 1,...,k-1, chooses p_k in P and q_k in Q, with the goal of confusing the hypothesis test. We prove that even in this case, the optimal exponential error rate can be…
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