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The thermal and statistical properties of hadronic matter under some extreme conditions are investigated using an exactly solvable canonical ensemble model. A unified model describing both the fragmentation of nuclei and the thermal…

Nuclear Theory · Physics 2008-11-26 K. C. Chase , A. Z. Mekjian , P. Meenakshisundaram

We establish one-body reduced density-matrix functional theory for the canonical ensemble in a finite basis set at an elevated temperature. Including temperature guarantees differentiability of the universal functional by occupying all…

Mathematical Physics · Physics 2023-02-22 Sarina M. Sutter , Klaas J. H. Giesbertz

We study the finite-temperature scrambling behavior of a quantum system described by a Hamiltonian chosen from a random matrix ensemble. This effectively (0+1)-dimensional model admits an exact calculation of various ensemble-averaged…

Strongly Correlated Electrons · Physics 2018-03-23 Sagar Vijay , Ashvin Vishwanath

The aim of this work is to introduce a thermo-electromagnetic model for calculating the temperature and the power dissipated in cylindrical pieces whose geometry var\'ies with time and undergoes large deformations; the motion will be a…

Numerical Analysis · Mathematics 2024-03-08 Marta Benítez , Alfredo Bermúdez , Pedro Fontán , Iván Martínez , Pilar Salgado

Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging. In particular, although ODEs are differentiable and would…

Machine Learning · Computer Science 2024-07-22 Jonas Beck , Nathanael Bosch , Michael Deistler , Kyra L. Kadhim , Jakob H. Macke , Philipp Hennig , Philipp Berens

In this work we present a formalism to describe non equilibrium conditions in systems with a discretized energy spectrum, such as quantum systems. We develop a formalism based on a combination of Gibbs-Shannon entropy and information…

Statistical Mechanics · Physics 2013-07-23 Alessio Gagliardi , Alessandro Pecchia , Aldo Di Carlo

We introduce 1-RDMFT in the canonical ensemble and then proceed to approximate the interacting ensemble by a non-interacting ensemble that maximizes the entropy, independently of temperature. Bosonic and Fermionic Sinkhorn algorithms are…

Chemical Physics · Physics 2022-05-31 Derk P. Kooi

We consider an ordinary differential equation with a unique hyperbolic attractor at the origin, to which we add a small random perturbation. It is known that under general conditions, the solution of this stochastic differential equation…

Probability · Mathematics 2023-05-05 Gerardo Barrera , Milton Jara

The Loschmidt echo is a popular quantity that allows making predictions about the stability of quantum states under time evolution. In our work, we present an approach that allows us to find a differential equation that can be used to…

Statistical Mechanics · Physics 2025-01-08 Michael Vogl

We present a variational density matrix approach to the thermal properties of interacting fermions in the continuum. The variational density matrix is parametrized by a permutation equivariant many-body unitary transformation together with…

Strongly Correlated Electrons · Physics 2022-04-04 Hao Xie , Linfeng Zhang , Lei Wang

An expansion method for perturbation of the zero temperature grand canonical density matrix is introduced. The method achieves quadratically convergent recursions that yield the response of the zero temperature density matrix upon variation…

Materials Science · Physics 2009-11-10 Anders M. N. Niklasson , Matt Challacombe

Previous approaches of emergent thermalization for condensed matter based on typical wavefunctions are extended to generate an intrinsically quantum theory of gases. Gases are fundamentally quantum objects at all temperatures, by virtue of…

General Physics · Physics 2015-06-29 Clifford Chafin

Dirac-Frenkel variational method with Davydov D2 trial wavefunction is extended by introducing a thermalization algorithm and applied to simulate dynamics of a general open quantum system. The algorithm allows to control temperature…

Quantum Physics · Physics 2021-03-04 Mantas Jakučionis , Darius Abramavičius

Thermodynamic computing exploits fluctuations and dissipation in physical systems to efficiently solve various mathematical problems. For example, it was recently shown that certain linear algebra problems can be solved thermodynamically,…

Statistical Mechanics · Physics 2024-01-08 Samuel Duffield , Maxwell Aifer , Gavin Crooks , Thomas Ahle , Patrick J. Coles

We consider the standard thermodynamic processes with constraints, but with additional uncertainty about the control parameters. Motivated by inductive reasoning, we assign prior distribution that provides a rational guess about likely…

Statistical Mechanics · Physics 2014-04-03 Preety Aneja , Ramandeep S. Johal

The aim of the present paper is to provide a preliminary investigation of the thermodynamics of particles obeying monotone statistics. To render the potential physical applications realistic, we propose a modified scheme called…

Mathematical Physics · Physics 2023-01-27 Fabio Ciolli , Francesco Fidaleo , Chiara Marullo

Scrambling and thermalisation are topics of intense study in both condensed matter and high energy physics. Random unitary dynamics form a simple testing-ground for our theoretical understanding of these processes. In this work, we derive…

Quantum Physics · Physics 2021-10-29 Ewan McCulloch

In a previous article we developed an approach to the optimal (minimum variance, unbiased) statistical estimation technique for the equilibrium displacement of a damped, harmonic oscillator in the presence of thermal noise. Here, we expand…

Data Analysis, Statistics and Probability · Physics 2009-09-28 Jason H. Steffen , Michael W. Moore , Paul E. Boynton

Out-of-time-order correlators (OTOCs) have become established as a tool to characterise quantum information dynamics and thermalisation in interacting quantum many-body systems. It was recently argued that the expected exponential growth of…

Statistical Mechanics · Physics 2021-09-21 Marlon Brenes , Silvia Pappalardi , Mark T. Mitchison , John Goold , Alessandro Silva

Most recent advances in machine learning and analytics for process control pose the question of how to naturally integrate new data-driven methods with classical process models and control. We propose a process modeling framework enabling…

Neural and Evolutionary Computing · Computer Science 2025-08-08 Michael R. Wartmann , B. Erik Ydstie