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Many-body localized (MBL) phases of disordered quantum many-particle systems have a number of unique properties, including failure to act as a thermal bath and protection of quantum coherence. Studying MBL is complicated by the effects of…

Disordered Systems and Neural Networks · Physics 2022-01-03 Michael Sonner , Alessio Lerose , Dmitry A. Abanin

The multiconfiguration time-dependent Hartree-Fock (MCTDHF) method is formulated for treating the coupled electronic and nuclear dynamics of diatomic molecules without the Born- Oppenheimer approximation. The method treats the full…

Computational Physics · Physics 2015-05-27 Daniel J. Haxton , Keith V. Lawler , C. William McCurdy

We investigate an approach for studying the ground state of a quantum many-body Hamiltonian that is based on treating the correlation functions as variational parameters. In this approach, the challenge set by the exponentially-large…

Strongly Correlated Electrons · Physics 2020-01-22 Arbel Haim , Richard Kueng , Gil Refael

We examine the interplay of interaction and disorder for a Heisenberg spin ladder system with random fields. We identify many-body localized states based on the entanglement entropy scaling, where delocalized and localized states have…

Strongly Correlated Electrons · Physics 2015-12-09 Elliott Baygan , S. P. Lim , D. N. Sheng

We review results about entanglement (or modular) Hamiltonians of quantum many-body systems in field theory and statistical mechanics models, as well as recent applications in the context of quantum information and quantum simulation.

Statistical Mechanics · Physics 2022-08-18 M. Dalmonte , V. Eisler , M. Falconi , B. Vermersch

Modeling quantum many-body systems is enormously challenging due to the exponential scaling of Hilbert dimension with system size. Finding efficient compressions of the wavefunction is key to building scalable models. Here, we introduce…

Computational Physics · Physics 2020-03-16 Christopher Roth

The study of nonequilibrium phenomena in correlated lattice systems has developed into an active and exciting branch of condensed matter physics. This research field provides rich new insights that could not be obtained from the study of…

Strongly Correlated Electrons · Physics 2014-07-11 Hideo Aoki , Naoto Tsuji , Martin Eckstein , Marcus Kollar , Takashi Oka , Philipp Werner

Phase transition in quantum many-body systems inevitably causes changes in certain physical properties which then serve as potential indicators of critical phenomena. Besides the traditional order parameters, characterization of quantum…

Quantum Physics · Physics 2017-08-23 Sudipto Singha Roy , Himadri Shekhar Dhar , Debraj Rakshit , Aditi Sen De , Ujjwal Sen

We study the dynamics of an interacting quantum spin chain under the application of a linearly increasing field. This model exhibits a type of localization known as Stark many-body localization. The dynamics shows a strong dependence on the…

Disordered Systems and Neural Networks · Physics 2021-03-10 Elmer V. H. Doggen , Igor V. Gornyi , Dmitry G. Polyakov

We explore the set of unitary matrices characterized by a given structure in the context of their applications in the field of Quantum Information. In the first part of the Thesis we focus on classification of special classes of unitary…

Quantum Physics · Physics 2022-04-27 Wojciech Bruzda

We use modified linear spin-wave theory (MLSWT) to study ground-state entanglement for a length-$L$ line subsystem in $L\times L$ square- and triangular-lattice quantum Heisenberg antiferromagnets with coplanar spiral magnetic order with…

Strongly Correlated Electrons · Physics 2020-05-19 Dag-Vidar Bauer , J. O. Fjærestad

Variational neural network models have achieved remarkable success in solving ground-state problems of quantum many-body systems. However, addressing classical and quantum spin glasses remains challenging, as disorder and energy frustration…

Disordered Systems and Neural Networks · Physics 2026-05-18 Luca Leone , Arka Dutta , Markus Heyl , Enrico Prati , Pietro Torta

Many biological and physical systems exhibit behaviour at multiple spatial, temporal or population scales. Multiscale processes provide challenges when they are to be simulated using numerical techniques. While coarser methods such as…

Quantitative Methods · Quantitative Biology 2018-02-12 Cameron A. Smith , Christian A. Yates

Dynamics of entanglement is investigated on the basis of exactly solvable models of multiple-quantum (MQ) NMR spin dynamics. It is shown that the time evolution of MQ coherences of systems of coupled nuclear spins in solids is directly…

Quantum Physics · Physics 2009-11-10 S. I. Doronin

Understanding the many-body dynamics of isolated quantum systems is one of the central challenges in modern physics. To this end, the direct experimental realization of strongly correlated quantum systems allows one to gain insights into…

Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds…

Quantum Physics · Physics 2025-01-22 Gyungmin Cho , Dohun Kim

Closure problems are omnipresent when simulating multiscale systems, where some quantities and processes cannot be fully prescribed despite their effects on the simulation's accuracy. Recently, scientific machine learning approaches have…

Numerical Analysis · Mathematics 2024-09-13 Benjamin Sanderse , Panos Stinis , Romit Maulik , Shady E. Ahmed

Many-body localization (MBL) addresses the absence of thermalization in interacting quantum systems, with non-ergodic high-energy eigenstates behaving as ground states, only area-law entangled. However, computing highly excited many-body…

Disordered Systems and Neural Networks · Physics 2019-01-23 Maxime Dupont , Nicolas Laflorencie

We investigate the two-dimensional frustrated quantum Heisenberg model with bond disorder on nearest-neighbor couplings using the recently introduced Foundation Neural-Network Quantum States framework, which enables accurate and efficient…

Variational quantum calculations have borrowed many tools and algorithms from the machine learning community in the recent years. Leveraging great expressive power and efficient gradient-based optimization, researchers have shown that trial…

Disordered Systems and Neural Networks · Physics 2024-08-19 Matija Medvidović , Javier Robledo Moreno