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For decades, people are developing efficient numerical methods for solving the challenging quantum many-body problem, whose Hilbert space grows exponentially with the size of the problem. However, this journey is far from over, as previous…

Accurately describing strongly correlated electrons in systems such as transition metal complexes requires strict adherence to spin symmetry, a feature largely absent in modern neural-network-based variational wavefunctions. This deficiency…

Chemical Physics · Physics 2026-04-09 Yunzhi Li , Zibo Wu , Bohan Zhang , Wei-Hai Fang , Zhendong Li

Ultracold atomic gases provide a fantastic platform to implement quantum simulators and investigate a variety of models initially introduced in condensed matter physics or other areas. One of the most promising applications of quantum…

Quantum Gases · Physics 2019-01-07 Leticia Tarruell , Laurent Sanchez-Palencia

Quantum simulators have made a remarkable progress towards exploring the dynamics of many-body systems, many of which offer a formidable challenge to both theoretical and numerical methods. While state-of-the-art quantum simulators are in…

Quantum many-body theory has witnessed tremendous progress in various fields, ranging from atomic and solid-state physics to quantum chemistry and nuclear structure. Due to the inherent computational burden linked to the ab initio treatment…

The quantum many-body problem lies at the center of the most important open challenges in condensed matter, quantum chemistry, atomic, nuclear, and high-energy physics. While quantum Monte Carlo, when applicable, remains the most powerful…

Strongly Correlated Electrons · Physics 2022-06-30 Hongwei Chen , Douglas Hendry , Phillip Weinberg , Adrian E. Feiguin

Tensor networks were developed in the context of many-body physics as compressed representations of multiparticle quantum states. These representations mitigate the exponential complexity of many-body systems by capturing only the most…

Machine Learning · Computer Science 2026-04-17 Guillermo Valverde , Igor García-Olaizola , Giannicola Scarpa , Alejandro Pozas-Kerstjens

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…

We present an alternative approach to decompose non-negative tensors, called many-body approximation. Traditional decomposition methods assume low-rankness in the representation, resulting in difficulties in global optimization and target…

Machine Learning · Statistics 2023-10-31 Kazu Ghalamkari , Mahito Sugiyama , Yoshinobu Kawahara

We develop a workflow to use current quantum computing hardware for solving quantum many-body problems, using the example of the fermionic Hubbard model. Concretely, we study a four-site Hubbard ring that exhibits a transition from a…

We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schr\"odinger equation in a systematically improvable fashion. We…

Nuclear Theory · Physics 2022-06-22 A. Lovato , C. Adams , G. Carleo , N. Rocco

We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems. This family consists of Slater determinants in an augmented Hilbert space involving "hidden"…

Strongly Correlated Electrons · Physics 2022-08-18 Javier Robledo Moreno , Giuseppe Carleo , Antoine Georges , James Stokes

We introduce an algorithmic framework based on tensor networks for computing fluid flows around immersed objects in curvilinear coordinates. We show that the tensor network simulations can be carried out solely using highly compressed…

Computing the ground state of interacting quantum matter is a long-standing challenge, especially for complex two-dimensional systems. Recent developments have highlighted the potential of neural quantum states to solve the quantum…

Disordered Systems and Neural Networks · Physics 2025-07-03 Ao Chen , Markus Heyl

Within the framework of Fermionic Molecular Dynamics a method is developed to better account for long range tensor correlations in nuclei when working with a single Slater determinant. Single-particle states with mixed isospin and broken…

Nuclear Theory · Physics 2008-11-26 Sonia Bacca , Hans Feldmeier , Thomas Neff

Many phenomena of strongly correlated materials are encapsulated in the Fermi-Hubbard model whose thermodynamical properties can be computed from its grand canonical potential according to standard procedures. In general, there is no closed…

Quantum Physics · Physics 2016-03-09 Pierre-Luc Dallaire-Demers , Frank K. Wilhelm

One of the challenging problems in the condensed matter physics is to understand the quantum many-body systems, especially, their physical mechanisms behind. Since there are only a few complete analytical solutions of these systems, several…

Statistical Mechanics · Physics 2020-03-27 Jozef Genzor , Tomotoshi Nishino , Andrej Gendiar

The quantum dynamics away from equilibrium is of fundamental interest for interacting many-body systems. In this letter, we study tilted many-body systems using the effective Hamiltonian derived from the microscopic description. We first…

Quantum Gases · Physics 2020-07-29 Pengfei Zhang

Such problems as computation of spectra of spin chains and vibrational spectra of molecules can be written as high-dimensional eigenvalue problems, i.e., when the eigenvector can be naturally represented as a multidimensional tensor. Tensor…

Numerical Analysis · Mathematics 2019-09-04 Maxim Rakhuba , Alexander Novikov , Ivan Oseledets