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Abstract: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 member of which is almost equally likely. This property is a form of the law of large numbers and lies at the heart of information theory. In this work, we prove a generalized quantum AEP beyond the i.i.d. framework where the random samples are drawn from two sets of quantum states. In particular, under suitable assumptions on the sets, we prove that all operationally relevant divergences converge to the quantum relative entropy between the sets. More specifically, both the quantum hypothesis testing relative entropy and the smoothed max-relative entropy approach the regularized relative entropy between the sets. Notably, the asymptotic limit has explicit convergence guarantees and can be efficiently estimated through convex optimization programs, despite the regularization, provided that the sets have efficient descriptions. The generalized AEP directly implies a new quantum Stein's lemma for conducting quantum hypothesis testing between two sets of quantum states. Combined with the accompanying work [IEEE TIT 72(4):2330-2342 (2026)], this addresses open questions raised by Brandao et al. [IEEE TIT 66(8):5037-5054 (2020)] and Mosonyi et al. [IEEE TIT 68(2):1032-1067 (2022)], which seek a Stein's lemma with computational efficiency. Moreover, we propose a new framework for quantum resource theory in which state transformations are performed without requiring precise characterization of the states being manipulated, making it more robust to imperfections. We demonstrate the reversibility (also referred to as the second law) of such a theory and identify the regularized relative entropy as the unique measure of the resource in this new framework.

Submission history

From: Kun Fang [view email]
[v1] Wed, 6 Nov 2024 16:33:16 UTC (1,062 KB)
[v2] Fri, 21 Feb 2025 17:55:29 UTC (654 KB)
[v3] Tue, 3 Jun 2025 14:44:02 UTC (1,283 KB)
[v4] Wed, 2 Sep 2026 15:44:37 UTC (1,286 KB)