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The Fermi-Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum…

Quantum Physics · Physics 2026-05-27 Talal Ahmed Chowdhury , Vladimir Korepin , Vincent R. Pascuzzi , Kwangmin Yu

Reliable simulations of correlated quantum systems, including high-temperature superconductors and frustrated magnets, are increasingly desired nowadays to further understanding of essential features in such systems. Quantum Monte Carlo…

Strongly Correlated Electrons · Physics 2019-03-28 Zi-Xiang Li , Hong Yao

We investigate the efficiency of the recently proposed Restricted Boltzmann Machine (RBM) representation of quantum many-body states to study both the static properties and quantum spin dynamics in the two-dimensional Heisenberg model on a…

Strongly Correlated Electrons · Physics 2019-07-10 G. Fabiani , J. H. Mentink

Many-body physics is one very well suited field for testing quantum algorithms and for finding working heuristics on present quantum computers. We have investigated the non-equilibrium dynamics of one- and two-electron systems, which are…

Quantum Physics · Physics 2022-07-06 Sabine Tornow , Wolfgang Gehrke , Udo Helmbrecht

Addressing the paramount need for precise calibration in superconducting quantum qubits, especially in frequency control, this study introduces a novel calibration scheme harnessing the principles of Many-Body Localization (MBL). While…

Quantum Physics · Physics 2023-10-17 Peng Qian , Hong-Ze Xu , Peng Zhao , Xiao Li , Dong E. Liu

A state-of-the-art method that combines a quantum computational algorithm and machine learning, so-called quantum machine learning, can be a powerful approach for solving quantum many-body problems. However, the research scope in the field…

Computational Physics · Physics 2023-04-04 Shu Kanno , Tomofumi Tada

We develop a systematic procedure for constructing quantum many-body problems whose spectrum can be partially or totally computed by purely algebraic means. The exactly-solvable models include rational and hyperbolic potentials related to…

Exactly Solvable and Integrable Systems · Physics 2008-11-26 D. Gomez-Ullate , A. Gonzalez-Lopez , M. A. Rodriguez

Simulating quantum many-body systems represents a fundamental challenge where classical machine learning methods are severely bottlenecked by the exponential curse of dimensionality. Variational Quantum Algorithms (VQAs) offer a native…

Quantum Physics · Physics 2026-03-31 Ge Yan , Kaisen Pan , Ruocheng Wang , Mengfei Ran , Hongxu Chen , Junchi Yan

The development of quantum control methods is an essential task for emerging quantum technologies. In general, the process of optimizing quantum controls scales very unfavorably in system size due to the exponential growth of the Hilbert…

Quantum Physics · Physics 2019-10-07 Jun Li

The famous, yet unsolved, Fermi-Hubbard model for strongly-correlated electronic systems is a prominent target for quantum computers. However, accurately representing the Fermi-Hubbard ground state for large instances may be beyond the…

The Hubbard model represents the fundamental model for interacting quantum systems and electronic correlations. Using the two-dimensional half-filled Hubbard model at weak coupling as a testing ground, we perform a comparative study of a…

Learning the structure of the entanglement Hamiltonian (EH) is central to characterizing quantum many-body states in analog quantum simulation. We describe a protocol where spatial deformations of the many-body Hamiltonian, physically…

Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous for pattern…

Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder generalization capability, and the scalability of quantum kernels…

Quantum Physics · Physics 2024-04-16 Rodrigo Martínez-Peña , Miguel C. Soriano , Roberta Zambrini

Atom-based quantum simulators have had tremendous success in tackling challenging quantum many-body problems, owing to the precise and dynamical control that they provide over the systems' parameters. They are, however, often optimized to…

In this work, we consider a fundamental task in quantum many-body physics - finding and learning ground states of quantum Hamiltonians and their properties. Recent works have studied the task of predicting the ground state expectation value…

Quantum Physics · Physics 2025-01-16 Štěpán Šmíd , Roberto Bondesan

Digital quantum simulation has broad applications in approximating unitary evolution of Hamiltonians. In practice, many simulation tasks for quantum systems focus on quantum states in the low-energy subspace instead of the entire Hilbert…

Quantum Physics · Physics 2024-07-16 Weiyuan Gong , Shuo Zhou , Tongyang Li

In a recent study[Phys. Rev. B 92 (2015) 125427], a hyperspherical approach has been developed to study of few-body fractional quantum Hall states. This method has been successfully applied to the exploration of few boson and fermion…

Strongly Correlated Electrons · Physics 2017-04-26 Bin Yan , Rachel E. Wooten , Kevin M. Daily , Chris H. Greene

The structure and dynamics of quantum many-body systems are the result of a delicate interplay between underlying interactions, which leads to intricate entanglement structures. Despite this apparent complexity, symmetries emerge and have…

Quantum Physics · Physics 2024-01-02 Marc Illa , Caroline E. P. Robin , Martin J. Savage

Calculating bounds of properties of many-body quantum systems is of paramount importance, since they guide our understanding of emergent quantum phenomena and complement the insights obtained from estimation methods. Recent semidefinite…

Quantum Physics · Physics 2026-01-16 Luke Mortimer , Leonardo Zambrano , Antonio Acín , Donato Farina
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