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We study the features extracted by the Restricted Boltzmann Machine (RBM) when it is trained with spin configurations of Ising model at various temperatures. Using the trained RBM, we obtain the flow of iterative reconstructions (RBM flow)…

Machine Learning · Computer Science 2021-11-23 Shotaro Shiba Funai

Bell's theorem states that no model that respects Local Causality and Statistical Independence can account for the correlations predicted by quantum mechanics via entangled states. This paper proposes a new approach, using backward-in-time…

Quantum Physics · Physics 2025-05-21 Simon Friederich

The present paper is a review of the phenomena related to non-equilibrium electron relaxation in bulk and nano-scale metallic samples. The workable Two-Temperature Model (TTM) based on Boltzmann-Bloch-Peierls (BBP) kinetic equation has been…

Statistical Mechanics · Physics 2009-03-23 Navinder Singh

A specific type of neural network, the Restricted Boltzmann Machine (RBM), is implemented for classification and feature detection in machine learning. RBM is characterized by separate layers of visible and hidden units, which are able to…

Disordered Systems and Neural Networks · Physics 2012-01-11 Adriano Barra , Alberto Bernacchia , Enrica Santucci , Pierluigi Contucci

The Bell theorem stands as an insuperable roadblock in the path to a very desired intuitive solution of the EPR paradox and, hence, it lies at the core of the current lack of a clear interpretation of the quantum formalism. The theorem…

Quantum Physics · Physics 2022-02-22 David H. Oaknin

Our investigation aims to study the specific role played by entanglement in the quantum computation process, by elaborating an entangled spin model developed within the 'hidden measurement approach' to quantum mechanics. We show that an…

Quantum Physics · Physics 2007-05-23 Diederik Aerts , Ellie D'Hondt , Bart D'Hooghe

Boltzmann Machines (BMs) are graphical models with interconnected binary units, employed for the unsupervised modeling of data distributions. When trained on real data, BMs show the tendency to behave like critical systems, displaying a…

Disordered Systems and Neural Networks · Physics 2024-06-28 Enrico Ventura , Simona Cocco , Rémi Monasson , Francesco Zamponi

Quantum computing raises the possibility of solving a variety of problems in physics that are presently intractable. A number of such problems involves the physics of systems in or near thermal equilibrium. There are two main ways to…

Quantum Physics · Physics 2023-08-16 Carter Ball , Thomas D. Cohen

The failure of Bell's theorem for Clifford algebra valued local variables is further consolidated by proving that the conditions of remote parameter independence and remote outcome independence are duly respected within the recently…

Quantum Physics · Physics 2007-08-22 Joy Christian

The no-signalling principle is a fundamental assumption in Bell-inequality and quantum-steering experiments. Nonetheless, experimental imperfections can lead to apparent violations beyond those expected from finite-sample statistics. Here,…

The subspace Restricted Boltzmann Machine (subspaceRBM) is a third-order Boltzmann machine where multiplicative interactions are between one visible and two hidden units. There are two kinds of hidden units, namely, gate units and subspace…

Machine Learning · Computer Science 2014-07-17 Jakub M. Tomczak , Adam Gonczarek

The quantum theory of coherent Ising machines, based on degenerate optical parametric oscillators and measurement-feedback circuits, is developed using the positive $P({\alpha},{\beta})$ representation of the density operator and the master…

Quantum Physics · Physics 2017-11-22 Taime Shoji , Kazuyuki Aihara , Yoshihisa Yamamoto

A general entangled qubit pair is analyzed in the de Broglie-Bohm formalism corresponding to two spin-1/2 quantum rotors. Several spin-spin correlators of Bohm's hidden variables are analyzed numerically and a detailed comparison with…

Quantum Physics · Physics 2015-06-04 A. Ramsak

An extreme learning machine (ELM) is a three-layered feed-forward neural network having untrained parameters, which are randomly determined before training. Inspired by the idea of ELM, a probabilistic untrained layer called a…

Machine Learning · Computer Science 2022-10-28 Yuri Kanno , Muneki Yasuda

We consider a quantum simulator of the Heisenberg chain with ferromagnetic interactions based on the two-component 1D Bose-Hubbard model at filling equal to two in the strong coupling regime. The entanglement properties of the ground state…

Quantum Gases · Physics 2019-07-03 Ivan Morera , Artur Polls , Bruno Juliá-Díaz

The Restricted Boltzmann Machine (RBM) is one of the simplest generative neural networks capable of learning input distributions. Despite its simplicity, the analysis of its performance in learning from the training data is only well…

Machine Learning · Computer Science 2025-11-13 Yizhou Xu , Florent Krzakala , Lenka Zdeborová

Geometric frustration gives rise to emergent quantum phenomena and exotic phases of matter. While Monte Carlo methods are traditionally used to simulate such systems, their sampling efficiency is limited by the complexity of interactions…

Statistical Mechanics · Physics 2025-11-27 Pratik Brahma , Junghoon Han , Tamzid Razzaque , Saavan Patel , Sayeef Salahuddin

Interactions between a quantum system and its environment at low temperatures can lead to violations of thermal laws for the system. The source of these violations is the entanglement between system and environment, which prevents the…

Other Condensed Matter · Physics 2015-05-20 Nathan S. Williams , Karyn Le Hur , Andrew N. Jordan

Time-reversal invariance plays a crucial role for many exotic quantum phases, particularly for topologically nontrivial states, in spin-orbit coupled electronic systems. Recently realized spin-orbit coupled cold-atom systems, however, lack…

Quantum Gases · Physics 2018-06-26 Matthew Maisberger , Lin-Cheng Wang , Kuei Sun , Yong Xu , Chuanwei Zhang

Restricted Boltzmann machines are used for probabilistic learning and are capable of capturing complex dependencies in data. They are employed for diverse purposes such as dimensionality reduction, feature learning and can be used for…

Mesoscale and Nanoscale Physics · Physics 2024-12-10 Rehana Begum Popy , Mahdis Hamdi , Robert L. Stamps