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Quantum technologies use entanglement to outperform classical technologies, and often employ strong cooling and isolation to protect entangled entities from decoherence by random interactions. Here we show that the opposite strategy -…

Topologically ordered quantum matter exhibits intriguing long-range patterns of entanglement, which reveal themselves in subsystem entropies. However, measuring such entropies, which can be used to certify topological order, on large…

Quantum Physics · Physics 2024-08-26 Robert Ott , Torsten V. Zache , Nishad Maskara , Mikhail D. Lukin , Peter Zoller , Hannes Pichler

The restricted Boltzmann machine is a network of stochastic units with undirected interactions between pairs of visible and hidden units. This model was popularized as a building block of deep learning architectures and has continued to…

Machine Learning · Computer Science 2018-06-20 Guido Montufar

Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as…

Neural and Evolutionary Computing · Computer Science 2016-07-20 Decebal Constantin Mocanu , Elena Mocanu , Phuong H. Nguyen , Madeleine Gibescu , Antonio Liotta

The past few years have witnessed the development of a comprehensive theory to describe integrable systems out of equilibrium, in which the Bethe ansatz formalism has been tailored to address specific problems arising in this context. While…

Statistical Mechanics · Physics 2019-09-30 Ranjan Modak , Lorenzo Piroli , Pasquale Calabrese

Topological electronic materials are new quantum states of matter hosting novel linear responses in the bulk and anomalous gapless states at the boundary, and are for scientific and applied reasons under intensive research in physics and in…

Materials Science · Physics 2019-03-01 Tiantian Zhang , Yi Jiang , Zhida Song , He Huang , Yuqing He , Zhong Fang , Hongming Weng , Chen Fang

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions…

Strongly Correlated Electrons · Physics 2018-02-07 Jing Chen , Song Cheng , Haidong Xie , Lei Wang , Tao Xiang

We propose a high-order stochastic-statistical moment closure model for efficient ensemble prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. The statistical moment…

Numerical Analysis · Mathematics 2023-06-21 Di Qi , Jian-Guo Liu

The R\'enyi entanglement entropy is calculated exactly for mode-partitioned isolated systems such as the two-mode squeezed state and the multi-mode Silbey-Harris polaron ansatz state. Effective thermodynamic descriptions of the correlated…

Quantum Physics · Physics 2015-12-24 King Karl R. Seroje , Rafael S. dela Rosa , Francis N. C. Paraan

Topologically ordered phase has emerged as one of most exciting concepts that not only broadens our understanding of phases of matter, but also has been found to have potential application in fault-tolerant quantum computation. The direct…

Quantum Physics · Physics 2016-06-01 Zhihuang Luo , Chao Lei , Jun Li , Xinfang Nie , Zhaokai Li , Xinhua Peng , Jiangfeng Du

We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schr\"odinger equation in configuration space. Traditional Configuration Interaction (CI) methods construct the wavefunction as a linear combination…

A hybrid quantum-classical method for learning Boltzmann machines (BM) for a generative and discriminative task is presented. Boltzmann machines are undirected graphs with a network of visible and hidden nodes where the former is used as…

Quantum Physics · Physics 2022-07-21 Siddhartha Srivastava , Veera Sundararaghavan

IIt is demonstrated that the entropy of statistical mechanics and of information theory, $S({\bf p}) = -\sum p_i \log p_i $ may be viewed as a measure of correlation. Given a probability distribution on two discrete variables, $p_{ij}$, we…

Mathematical Physics · Physics 2007-05-23 John H. Van Drie

In this work, we analyze the nonequilibrium thermodynamics of a class of neural networks known as Restricted Boltzmann Machines (RBMs) in the context of unsupervised learning. We show how the network is described as a discrete Markov…

Statistical Mechanics · Physics 2017-08-23 Domingos S. P. Salazar

Topological quantum many-body systems, such as Hall insulators, are characterized by a hidden order encoded in the entanglement between their constituents. Entanglement entropy, an experimentally accessible single number that globally…

We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes maximum entropy principle and from…

Machine Learning · Computer Science 2012-12-12 Shaojun Wang , Dale Schuurmans , Fuchun Peng , Yunxin Zhao

Correlation has been widely used to facilitate various information retrieval methods such as query expansion, relevance feedback, document clustering, and multi-modal fusion. Especially, correlation and independence are important issues…

Information Retrieval · Computer Science 2018-10-29 Dimitris Gkoumas , Sagar Uprety , Dawei Song

The recent developments in the study of topological multi-boundary entanglement in the context of 3d Chern-Simons theory (with gauge group $G$ and level $k$) suggest a strong interplay between entanglement measures and number theory. The…

High Energy Physics - Theory · Physics 2026-03-17 Siddharth Dwivedi

Dynamical evolution of quantum mechanical squeezing and entanglement in a two-mode Bose-Einstein condensate (TBEC) with an adiabatic, time-varying Raman coupling is studied by finding analytical expressions for these quantities. In…

Other Condensed Matter · Physics 2009-11-11 S. Choi , N. P. Bigelow

Predicting the dynamical properties of topological matter is a challenging task, not only in theoretical and experimental settings, but also numerically. This work proposes a variational approach based on a time-dependent correlated Ansatz,…

Quantum Physics · Physics 2025-08-05 Linda Mauron , Zakari Denis , Jannes Nys , Giuseppe Carleo
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