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Tensor network contraction is a powerful computational tool in quantum many-body physics, quantum information and quantum chemistry. The complexity of contracting a tensor network is thought to mainly depend on its entanglement properties,…

Quantum Physics · Physics 2025-12-11 Jiaqing Jiang , Jielun Chen , Norbert Schuch , Dominik Hangleiter

Entanglement, and quantum correlation, are precious resources for quantum technologies implementation based on quantum information science, such as, for instance, quantum communication, quantum computing, and quantum interferometry.…

Quantum Physics · Physics 2023-05-22 Arthur Vesperini , Ghofrane Bel-Hadj-Aissa , Roberto Franzosi

For pure states of multi-dimensional quantum lattice systems, which in a convenient computational basis have amplitude and phase structure of sufficiently rapid decorrelation, we construct high fidelity approximations of relatively low…

Quantum Physics · Physics 2025-03-18 Michael Aizenman , Simone Warzel

We give a capacity formula for the classical communication over a noisy quantum channel, when local operations and global permutations allowed in the encoding and bipartite states preshared between the sender and the receiver. The two…

Quantum Physics · Physics 2020-01-10 Kun Wang , Masahito Hayashi

Local operations assisted by classical communication (LOCC) constitute the free operations in entanglement theory. Hence, the determination of LOCC transformations is crucial for the understanding of entanglement. We characterize here…

Quantum Physics · Physics 2018-08-01 David Sauerwein , Nolan R. Wallach , Gilad Gour , Barbara Kraus

We propose an entanglement criterion, specially designed for mixed states, based on uncertainty relation and the Wigner-Yanase skew information. The variances in this uncertainty relation does not involve any classical mixing uncertainty,…

Quantum Physics · Physics 2022-11-01 Manju Maan , Asoka Biswas , Shubhrangshu Dasgupta

We present a bound for value-prediction error with respect to model misspecification that is tight, including constant factors. This is a direct improvement of the "simulation lemma," a foundational result in reinforcement learning. We…

Machine Learning · Computer Science 2024-10-28 Sam Lobel , Ronald Parr

Quantum entanglement is the quantum information processing resource. Thus it is of importance to understand how much of entanglement particular quantum states have, and what kinds of laws entanglement and also transformation between…

Quantum Physics · Physics 2007-05-23 Toshiyuki Shimono

We derive nearly tight and non-asymptotic convergence bounds for solutions of entropic semi-discrete optimal transport. These bounds quantify the stability of the dual solutions of the regularized problem (sometimes called Sinkhorn…

Artificial Intelligence · Computer Science 2022-05-05 Alex Delalande

Article presents general formulation of entanglement measures problem in terms of correlation function. Description of entanglement in probabilistic framework allow us to introduce new quantity which describes quantum and classical…

Quantum Physics · Physics 2007-05-23 S. Bugajski , J. A. Miszczak

Compact expressions for the average subentropy and coherence are obtained for random mixed states that are generated via various probability measures. Surprisingly, our results show that the average subentropy of random mixed states…

Quantum Physics · Physics 2017-02-08 Lin Zhang , Uttam Singh , Arun Kumar Pati

We study asymptotic state transformations in continuous variable quantum resource theories. In particular, we prove that monotones displaying lower semicontinuity and strong superadditivity can be used to bound asymptotic transformation…

Quantum Physics · Physics 2023-04-27 Giovanni Ferrari , Ludovico Lami , Thomas Theurer , Martin B. Plenio

We quantify the one-shot entanglement cost of an arbitrary bipartite state, that is the minimum number of singlets needed by two distant parties to create a single copy of the state up to a finite accuracy, using local operations and…

Quantum Physics · Physics 2011-04-01 Francesco Buscemi , Nilanjana Datta

Classical Edgeworth expansions provide asymptotic correction terms to the Central Limit Theorem (CLT) up to an order that depends on the number of moments available. In this paper, we provide subsequent correction terms beyond those given…

Probability · Mathematics 2011-03-23 Henry Lam , Jose Blanchet , Damian Burch , Martin Z. Bazant

We show that the usefulness of a state as an activator in teleportation protocols is equivalent to the robustness of its entanglement to noise. The robustness of entanglement of a bi-partite state is linked to the maximum increase in the…

Quantum Physics · Physics 2010-03-10 Fernando G. S. L. Brandao

The distribution of typical bipartite pure states is studied within the framework of state transformation via local operation and classical communication (LOCC). We report the statistics of comparable and incomparable states in different…

Quantum Physics · Physics 2022-11-14 Rivu Gupta , Arghya Maity , Shiladitya Mal , Aditi Sen De

The optimal rate of reliable communication over a quantum channel can be enhanced by pre-shared entanglement. Whereas the enhancement may be unbounded in infinite-dimensional settings even when the input power is constrained, a…

We provide an interpretation of entanglement based on classical correlations between measurement outcomes of complementary properties: states that have correlations beyond a certain threshold are entangled. The reverse is not true, however.…

Quantum Physics · Physics 2015-05-26 Lorenzo Maccone , Dagmar Bruss , Chiara Macchiavello

In this paper, we reconsider the large-argument asymptotic expansions of the Hankel, Bessel and modified Bessel functions and their derivatives. New integral representations for the remainder terms of these asymptotic expansions are found…

Classical Analysis and ODEs · Mathematics 2017-07-07 Gergő Nemes

Previous work has separately addressed different forms of action, state and action-state entropy regularization, pure exploration and space occupation. These problems have become extremely relevant for regularization, generalization,…

Machine Learning · Computer Science 2023-02-03 Dmytro Grytskyy , Jorge Ramírez-Ruiz , Rubén Moreno-Bote
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