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Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form. In practice, representing quantum-mechanical states of a given Hamiltonian using available…

Quantum Physics · Physics 2020-11-10 Andrey Kardashin , Alexey Uvarov , Dmitry Yudin , Jacob Biamonte

The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge…

Cosmology and Nongalactic Astrophysics · Physics 2024-03-22 Evan Saraivanov , Kunhao Zhong , Vivian Miranda , Supranta S. Boruah , Tim Eifler , Elisabeth Krause

Projected Entangled Pair States (PEPS) are recognized as a potent tool for exploring two-dimensional quantum many-body systems. However, a significant challenge emerges when applying conventional PEPS methodologies to systems with periodic…

Strongly Correlated Electrons · Physics 2024-07-23 Shaojun Dong , Chao Wang , Hao Zhang , Meng Zhang , Lixin He

Understanding the mechanisms responsible for the equilibration of isolated quantum many-body systems is a long-standing open problem. In this work we obtain a statistical relationship between the equilibration properties of Hamiltonians and…

Quantum Physics · Physics 2013-03-27 Lluis Masanes , Augusto J. Roncaglia , Antonio Acin

Perturbation theory with respect to the kinetic energy of the heavy component of a two-component quantum system is introduced. An effective Hamiltonian that is accurate to second order in the inverse heavy mass is derived. It contains a new…

Quantum Physics · Physics 2024-06-21 Ryan Requist

In electronic structure theory, variational methods offer a valuable paradigm for approximating electronic ground states. However, for historical reasons, this principle is mostly restricted to model chemistries in pre-defined fixed basis…

Quantum Physics · Physics 2025-11-17 Fabian Langkabel , Stefan Knecht , Jakob S. Kottmann

Calculating ground and excited states is an exciting prospect for near-term quantum computing applications, and accurate and efficient algorithms are needed to assess viable directions. We develop an excited state approach based on the…

Quantum Physics · Physics 2024-09-09 Scott E. Smart , Davis M. Welakuh , Prineha Narang

General analytic energy bounds are derived for N-boson systems governed by semirelativistic Hamiltonians of the form H=\sum_{i=1}^N \sqrt(p_i^2+m^2) + \sum_{1=i<j}^N V(r_{ij}), where V(r) is a static attractive pair potential. A…

Mathematical Physics · Physics 2008-11-26 Richard L. Hall , Wolfgang Lucha

We present an approach to construct appropriate and efficient emulators for Hamiltonian flow maps. Intended future applications are long-term tracing of fast charged particles in accelerators and magnetic plasma confinement configurations.…

Computational Physics · Physics 2021-06-02 Katharina Rath , Christopher G. Albert , Bernd Bischl , Udo von Toussaint

One of the most used approaches in simulating materials is the tight-binding approximation. When using this method in a material simulation, it is necessary to compute the eigenvalues and eigenvectors of the Hamiltonian describing the…

Numerical Analysis · Computer Science 2009-10-29 Matthias Petschow , Edoardo Di Napoli , Paolo Bientinesi

Recently, the full version of the Eigenstate Thermalization Hypothesis (ETH) has been systematized using Free Probability. In this paper, we present a detailed discussion of the Free Cumulants approach to many-body dynamics within the…

Statistical Mechanics · Physics 2025-02-19 Felix Fritzsch , Tomaž Prosen , Silvia Pappalardi

The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted…

Quantum Physics · Physics 2026-05-19 Jiaji Zhang , Lipeng Chen , Carlos L. Benavides-Riveros

Many applications require that we learn the parameters of a model from data. EM is a method used to learn the parameters of probabilistic models for which the data for some of the variables in the models is either missing or hidden. There…

Machine Learning · Computer Science 2013-01-30 Luis E. Ortiz , Leslie Pack Kaelbling

Due to critical environmental and technological issues, there is a pressing need to switch from current refrigeration methods based on compression of gases to novel solid-state cooling technologies. Solid-state cooling is based on the…

Materials Science · Physics 2020-05-12 Claudio Cazorla

I argue that certain bosonic insulator-superfluid phase transitions as an interaction constant varies are driven by emergent geometric properties of insulating states. The {\em renormalized} chemical potential and distribution of disordered…

Disordered Systems and Neural Networks · Physics 2016-08-31 Fei Zhou

The thermalizing dynamics of many-body systems is often described through the lens of the Eigenstate Thermalization Hypothesis (ETH). ETH postulates that the statistical properties of observables, when expressed in the energy eigenbasis,…

Statistical Mechanics · Physics 2026-04-01 Jiaozi Wang , Ruchira Mishra , Tian-Hua Yang , Luca V. Delacrétaz , Silvia Pappalardi

Adaptive precision molecular dynamics simulations have developed along energy- and force-coupling approaches, which allow for a continuous transition between different particle descriptions or interaction potentials. Most approaches…

Computational Physics · Physics 2025-12-09 David Immel , Ralf Drautz , Godehard Sutmann

Dynamical instabilities can amplify small perturbations into measurable signals, offering a route to quantum-enhanced sensing. This mechanism was experimentally demonstrated in a collective-spin system with quadratic interactions, described…

Quantum Physics · Physics 2026-04-08 Bidhi Vijaywargia , Jorge Chávez-Carlos , Francisco Pérez-Bernal , Lea F. Santos

Entropic dynamics (ED) is a framework that allows one to derive quantum theory as a Hamilton-Killing flow on the cotangent bundle of a statistical manifold. These flows are such that they preserve the symplectic and the (information) metric…

Quantum Physics · Physics 2025-11-25 Ariel Caticha

Fluid dynamics plays a crucial role in various multiphysics applications, including energy systems, electronics cooling, and biomedical engineering. Developing models for complex coupled systems can be challenging and time-consuming. In…

Computational Engineering, Finance, and Science · Computer Science 2024-12-09 Markus Lohmayer , Michael Kraus , Sigrid Leyendecker
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