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Most non-relativistic interacting quantum many-body systems, such as atomic and molecular ensembles or materials, are naturally described in terms of continuous-space Hamiltonians. The simulation of their ground-state properties on digital…

Quantum Physics · Physics 2024-09-11 Friederike Metz , Gabriel Pescia , Giuseppe Carleo

Solving the time-dependent Schr\"odinger equation (TDSE) is pivotal for modeling non-adiabatic electron dynamics, a key process in ultrafast spectroscopy and laser-matter interactions. However, exact solutions to the TDSE remain…

Chemical Physics · Physics 2025-12-29 Bizi Huang , Weizhong Fu , Ji Chen

We develop a Monte Carlo wave function algorithm for the quantum linear Boltzmann equation, a Markovian master equation describing the quantum motion of a test particle interacting with the particles of an environmental background gas. The…

Quantum Physics · Physics 2010-09-28 Marc Busse , Piotr Pietrulewicz , Heinz-Peter Breuer , Klaus Hornberger

Representing a target quantum state by a compact, efficient variational wave-function is an important approach to the quantum many-body problem. In this approach, the main challenges include the design of a suitable variational ansatz and…

Strongly Correlated Electrons · Physics 2018-12-03 Dmitrii Kochkov , Bryan K. Clark

Within the framework of probability distributions on projective Hilbert space a scheme for the calculation of multitime correlation functions is developed. The starting point is the Markovian stochastic wave function description of an open…

Quantum Physics · Physics 2009-10-31 Heinz-Peter Breuer , Bernd Kappler , Francesco Petruccione

Neural-network quantum states (NQS) employ artificial neural networks to encode many-body wave functions in second quantization through variational Monte Carlo (VMC). They have recently been applied to accurately describe electronic wave…

Chemical Physics · Physics 2023-11-27 Xiang Li , Jia-Cheng Huang , Guang-Ze Zhang , Hao-En Li , Chang-su Cao , Dingshun Lv , Han-Shi Hu

Initially developed in the framework of quantum stochastic calculus, the main equations of quantum stochastic filtering were later on derived as the limits of Markov models of discrete measurements under appropriate scaling. In many…

Mathematical Physics · Physics 2020-08-18 Vassili N. Kolokoltsov

The dissipation and decoherence (for example, the effects of noise in quantum computations), interaction with thermostat or in general with physical vacuum, measurement and many other complicated problems of open quantum systems are a…

Mathematical Physics · Physics 2007-05-23 Ashot S. Gevorkyan

Quantum stochastic differential equations have been used to describe the dynamics of an atom interacting with the electromagnetic field via absorption/emission processes. Here, by using the full quantum stochastic Schroedinger equation…

Quantum Physics · Physics 2009-10-31 Alberto Barchielli , Giancarlo Lupieri

A novel general formalism for the maximal symetrization and reduction of fields (MSRF) is proposed and applied to wavefunctions in solid state nanostructures. Its primary target is to provide an essential tool for the study and analysis of…

Mesoscale and Nanoscale Physics · Physics 2015-05-14 S. Dalessi , M. -A. Dupertuis

Treating the fermionic ground state problem as a constrained stochastic optimization problem, a formalism for fermionic quantum Monte Carlo is developed that makes no reference to a trial wavefunction. Exchange symmetry is enforced by…

Strongly Correlated Electrons · Physics 2020-10-14 Michael Hutcheon

Quasiparticle (QP) excitations are extremely important for understanding and predicting charge transfer and transport in molecules, nanostructures and extended systems. Since density functional theory (DFT) within the Kohn-Sham (KS)…

Chemical Physics · Physics 2017-07-18 Vojtech Vlcek , Eran Rabani , Daniel Neuhauser , Roi Baer

The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using…

An efficient algorithm to simulate dynamics of open quantum system is presented. The method describes the dynamics by unraveling stochastic wave functions converging to a density operator description. The stochastic techniques are based on…

Quantum Physics · Physics 2022-09-21 Ronnie Kosloff Uriel Shafir

The state space representation of active resident space objects can be posed in the form of a stochastic hybrid system. Satellite maneuvers may be accounted for according to control cost or heuristical considerations, yet it is possible to…

Signal Processing · Electrical Eng. & Systems 2022-04-06 Guillermo Escribano , Manuel Sanjurjo-Rivo , Jan Siminski , Alejandro Pastor , Diego Escobar

Variational wave function ansatze are an invaluable tool to study the properties of strongly correlated systems. We propose such a wave function, based on the theory of auxiliary fields and combining aspects of auxiliary-field quantum Monte…

Strongly Correlated Electrons · Physics 2024-03-13 Ryan Levy , Miguel A. Morales , Shiwei Zhang

We develop a machine learning method to construct accurate ground-state wave functions of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A restricted Boltzmann machine algorithm in the form of an…

Strongly Correlated Electrons · Physics 2017-11-30 Yusuke Nomura , Andrew S. Darmawan , Youhei Yamaji , Masatoshi Imada

We develop a system of non-linear stochastic evolution equations that describes the continuous measurements of quantum systems with mixed initial state. We address quantum systems with unbounded Hamiltonians and unbounded interaction…

Mathematical Physics · Physics 2025-03-20 Carlos M. Mora

The computation of the symmetric regularized-long-wave (SRLW) equation, which describes weekly nonlinear ion acoustic and space-charge waves, is dealt with in this paper. The numerical scheme to be proposed applies the Fourier…

Mathematical Physics · Physics 2013-08-21 Xuanchun Dong

Modeling non-stationary processes, where statistical properties vary across the input domain, is a critical challenge in machine learning; yet most scalable methods rely on a simplifying assumption of stationarity. This forces a difficult…

Machine Learning · Computer Science 2026-02-03 Sawan Kumar , Souvik Chakraborty
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