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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…

Quantum Physics · Physics 2025-11-18 Kun Fang , Masahito Hayashi

The performance of quantum resource manipulation protocols, including key examples such as distillation of quantum entanglement, is measured in terms of the rate at which desired target states can be produced from a given noisy state.…

Quantum Physics · Physics 2026-05-15 Ludovico Lami , Bartosz Regula , Ryuji Takagi

In this MSc thesis I consider the asymptotic behaviour of the symmetric error in composite hypothesis testing. In the classical case, when the null and alternative hypothesis are finite sets of states, the best achievable symmetric error…

Quantum Physics · Physics 2020-11-09 Zsombor Szilágyi

The asymptotic equipartition property (AEP) states that in the limit of a large number of independent and identically distributed (i.i.d.) random experiments, the output sequence is virtually certain to come from the typical set, each…

Quantum Physics · Physics 2025-06-04 Kun Fang , Hamza Fawzi , Omar Fawzi

We characterize the asymptotic performance of nonparametric goodness of fit testing. The exponential decay rate of the type-II error probability is used as the asymptotic performance metric, and a test is optimal if it achieves the maximum…

Machine Learning · Statistics 2019-03-19 Shengyu Zhu , Biao Chen , Pengfei Yang , Zhitang Chen

We study a variant of quantum hypothesis testing wherein an additional 'inconclusive' measurement outcome is added, allowing one to abstain from attempting to discriminate the hypotheses. The error probabilities are then conditioned on a…

Quantum Physics · Physics 2024-07-09 Bartosz Regula , Ludovico Lami , Mark M. Wilde

This paper formally derives the asymptotic distribution of a goodness-of-fit test based on the Kernel Stein Discrepancy introduced in (Oscar Key et al., "Composite Goodness-of-fit Tests with Kernels", Journal of Machine Learning Research…

Statistics Theory · Mathematics 2026-02-24 Florian Brück , Veronika Reimoser , Fabian Baier

We characterize the asymptotic performance of nonparametric one- and two-sample testing. The exponential decay rate or error exponent of the type-II error probability is used as the asymptotic performance metric, and an optimal test…

Information Theory · Computer Science 2021-02-08 Shengyu Zhu , Biao Chen , Zhitang Chen , Pengfei Yang

In this paper we revisit the binary hypothesis testing problem with one-sided compression. Specifically we assume that the distribution in the null hypothesis is a mixture distribution of iid components. The distribution under the…

Information Theory · Computer Science 2022-07-07 Minh Thanh Vu

We present a generalization of quantum Stein's Lemma to the situation in which the alternative hypothesis is formed by a family of states, which can moreover be non-i.i.d.. We consider sets of states which satisfy a few natural properties,…

Quantum Physics · Physics 2010-03-10 Fernando G. S. L. Brandao , Martin B. Plenio

In this paper, we treat an asymptotic hypothesis testing (or state discrimination with asymmetric treatment of errors) between an arbitrary fixed bipartite pure state and the completely mixed state by one-way LOCC, two-way LOCC, and…

Quantum Physics · Physics 2020-10-06 Masaki Owari , Masahito Hayashi

A lemma stated by Ke Li in [arXiv:1208.1400] has been used in e.g. [arXiv:1510.04682,arXiv:1706.04590,arXiv:1612.01464,arXiv:1308.6503,arXiv:1602.08898] for various tasks in quantum hypothesis testing, data compression with quantum side…

Mathematical Physics · Physics 2023-01-10 Yan Pautrat , Simeng Wang

In the simple quantum hypothesis testing problem, upper bounds on the error probabilities are shown based on a key operator inequality between a density operator and its pinching. Concerning the error exponents, the upper bounds lead to a…

Quantum Physics · Physics 2007-05-23 Tomohiro Ogawa , Masahito Hayashi

We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is to compare two models, both of which may have unobserved latent variables, such that the marginal distribution of the observed variables is…

Machine Learning · Statistics 2023-05-10 Heishiro Kanagawa , Wittawat Jitkrittum , Lester Mackey , Kenji Fukumizu , Arthur Gretton

We derive a necessary and sufficient condition for a sequence of quantum measurements to achieve the optimal performance in quantum hypothesis testing. Using an information-spectrum method, we discuss what quantum measurement we should…

Quantum Physics · Physics 2009-11-07 Masahito Hayashi

We propose a simple and intuitive test for arguably the most prevailing hypothesis in statistics that data are independent and identically distributed (IID), based on a newly introduced off-diagonal sequential U-process. This IID test is…

Methodology · Statistics 2025-06-30 Tongyu Li , Jonas Mueller , Fang Yao

This paper deals with order identification for nested models in the i.i.d. framework. We study the asymptotic efficiency of two generalized likelihood ratio tests of the order. They are based on two estimators which are proved to be…

Statistics Theory · Mathematics 2007-06-13 Antoine Chambaz

Even though quantum information theory gives advantage over classical information theory, these two information theories have a structural similarity that many exponet rates of information tasks asymptotically equal to entropic quantities.…

Quantum Physics · Physics 2025-05-06 Kanta Sonoda , Hayato Arai , Masahito Hayashi

The trade-off between the two types of errors in binary state discrimination may be quantified in the asymptotics by various error exponents. In the case of simple i.i.d. hypotheses, each of these exponents is equal to a divergence…

Quantum Physics · Physics 2023-01-18 Milán Mosonyi , Zsombor Szilágyi , Mihály Weiner

The classical binary hypothesis testing problem is revisited. We notice that when one of the hypotheses is composite, there is an inherent difficulty in defining an optimality criterion that is both informative and well-justified. For…

Statistics Theory · Mathematics 2021-03-29 Michael Bell , Yuval Kochman