English
Related papers

Related papers: Estimating quantum relative entropies on quantum c…

200 papers

Paramount for performances of quantum network applications are the structure and quality of distributed entanglement. Here we propose a scalable and efficient approach to reveal the topological information of unknown quantum networks, and…

Quantum Physics · Physics 2025-07-15 Jun-Hao Wei , Xin-Yu Xu , Shu-Ming Hu , Nuo-Ya Yang , Li Li , Nai-Le Liu , Kai Chen

We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual information, which are fundamental properties in quantum information theory.…

Quantum Physics · Physics 2024-02-13 Myeongjin Shin , Junseo Lee , Kabgyun Jeong

Quantum Boltzmann machine extends the classical Boltzmann machine learning to the quantum regime, which makes its power to simulate the quantum states beyond the classical probability distributions. We develop the BFGS algorithm to study…

Quantum Physics · Physics 2019-04-10 Hai-Jing Song , Tieling Song , Qi-Kai He , Yang Liu , D. L. Zhou

The problem of quantum state inference and the concept of quantum entanglement are studied using a non-additive measure in the form of Tsallis entropy indexed by the positive parameter q. The maximum entropy principle associated with this…

Quantum Physics · Physics 2009-10-31 Sumiyoshi Abe , A. K. Rajagopal

The study of quantum and classical correlations between subsystems is fundamental to understanding many-body physics. In quantum information theory, the quantum mutual information, $I(A;B)$, is a measure of correlation between the…

Quantum Physics · Physics 2026-01-21 Uri Levin , Noa Feldman , Moshe Goldstein

We study quantum measurement retrodiction using the principle of minimum change. For quantum-to-classical measurement channels, we show that all standard quantum divergences select the same retrodictive update, yielding a unique and…

Quantum Physics · Physics 2025-11-27 Jiaxi Kuang , Kensei Torii , Francesco Buscemi

Present quantum computers are constrained by limited qubit capacity and restricted physical connectivity, leading to challenges in large-scale quantum computations. Distributing quantum computations across a network of quantum computers is…

Quantum Physics · Physics 2024-05-14 Ranjani G Sundaram , Himanshu Gupta , C. R. Ramakrishnan

We compute analytically the statistics of the Renyi and von Neumann entropies (standard measures of entanglement), for a random pure state in a large bipartite quantum system. The full probability distribution is computed by first mapping…

Statistical Mechanics · Physics 2011-05-30 Celine Nadal , Satya N Majumdar , Massimo Vergassola

Estimating a quantum phase is a necessary task in a wide range of fields of quantum science. To accomplish this task, two well-known methods have been developed in distinct contexts, namely, Ramsey interferometry (RI) in atomic and…

Conventionally the total correlations within a quantum system are quantified through distance-based expressions such as the relative entropy or the square-norm. Those expressions imply that a quantum state can contain both classical and…

Quantum Physics · Physics 2025-03-13 Spyros Tserkis , Syed M. Assad , Ping Koy Lam , Prineha Narang

Quantum coherence is the most distinguished signature of quantum mechanics, also recognized to be an essential resource for many promising quantum technologies, playing a central role in phenomena related to quantum information science,…

Quantum Physics · Physics 2019-02-27 Bertúlio de Lima Bernardo

Quantum causality is an emerging field of study which has the potential to greatly advance our understanding of quantum systems. In this paper, we put forth a theoretical framework for merging quantum information science and causal…

Quantum Physics · Physics 2022-12-21 Mohammad Ali Javidian , Vaneet Aggarwal , Zubin Jacob

We present first measure of quantum correlation of an ensemble of multiparty states. It is based on the idea of minimal entropy production in a locally distinguishable basis measurement. It is shown to be a relative entropy distance from a…

Quantum Physics · Physics 2009-10-29 Michal Horodecki , Aditi Sen De , Ujjwal Sen

Proton transfer reactions are fundamental to many chemical and biological systems, where quantum effects such as tunneling, delocalization, and zero-point motion play key kinetic control roles. However, classical methods capable of…

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size.…

Quantum Physics · Physics 2022-08-02 Shichuan Xue , Yong Liu , Yang Wang , Pingyu Zhu , Chu Guo , Junjie Wu

In quantum computing the decoherence time of the qubits determines the computation time available and this time is very limited when using current hardware. In this paper we minimize the execution time (the depth) for a class of circuits…

A milestone in the field of quantum computing will be solving problems in quantum chemistry and materials faster than state-of-the-art classical methods. The current understanding is that achieving quantum advantage in this area will…

Quantum Physics · Physics 2023-11-08 Guoming Wang , Daniel Stilck França , Ruizhe Zhang , Shuchen Zhu , Peter D. Johnson

A characteristic feature of "quantum chaotic" systems is that their eigenspectra and eigenstates display universal statistical properties described by random matrix theory (RMT). However, eigenstates of local systems also encode structure…

Statistical Mechanics · Physics 2024-09-25 Joaquin F. Rodriguez-Nieva , Cheryne Jonay , Vedika Khemani

We consider a Quantum Computer with n quantum-bits (`qubits'), where each qubit is coupled independently to an environment affecting the state in a dephasing or depolarizing way. For mixed states we suggest a quantification for the property…

Quantum Physics · Physics 2008-12-18 Dominik Janzing , Thomas Beth

Estimating the Kullback-Leibler (KL) divergence between random variables is a fundamental problem in statistical analysis. For continuous random variables, traditional information-theoretic estimators scale poorly with dimension and/or…

Machine Learning · Computer Science 2025-10-08 Mikil Foss , Andrew Lamperski