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In the present manuscript, we employ the Feynman path integral method to derive the propagator in one-dimensional Wigner-Dunkl quantum mechanics. To verify our findings we calculate the propagator associated with the free particle and the…

Quantum Physics · Physics 2024-10-01 A. Benchikha , B. Hamil , B. C. Lütfüoğlu , B. Khantoul

Boltzmann Generators have emerged as a promising machine learning tool for generating samples from equilibrium distributions of molecular systems using Normalizing Flows and importance weighting. Recently, Flow Matching has helped speed up…

Machine Learning · Statistics 2025-10-21 Lorenz Vaitl , Leon Klein

These lectures are intended as an introduction to the technique of path integrals and their applications in physics. The audience is mainly first-year graduate students, and it is assumed that the reader has a good foundation in quantum…

Quantum Physics · Physics 2007-05-23 Richard MacKenzie

We apply the method of flow equations to describe quantum systems subject to a time-periodic drive with a time-dependent envelope. The driven Hamiltonian is expressed in terms of its constituent Fourier harmonics with amplitudes that may…

Quantum Physics · Physics 2022-01-12 Viktor Novičenko , Giedrius Žlabys , Egidijus Anisimovas

We present a new formulation for the emergence of classical dynamics in a quantum world by considering a path integral approach that also incorporates continuous measurements. Our program is conceptually different from the decoherence…

Quantum Physics · Physics 2025-05-23 Harsh Arora , Bishal Kumar Das , Baladitya Suri , Vaibhav Madhok

The Feynman path integral for the generalized harmonic oscillator is reviewed, and it is shown that the path integral can be used to find a complete set of wave functions for the oscillator. Harmonic oscillators with different…

Quantum Physics · Physics 2007-05-23 Dae-Yup Song

Generative models realized with machine learning techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion models are an emerging…

Quantum Physics · Physics 2024-07-18 Marco Parigi , Stefano Martina , Filippo Caruso

We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for…

Machine Learning · Computer Science 2023-02-09 Yaron Lipman , Ricky T. Q. Chen , Heli Ben-Hamu , Maximilian Nickel , Matt Le

Generative modeling provides a powerful framework for learning data distributions. These models initially relied on probabilistic methods such as Gaussian Processes (GP) for uncertainty-aware predictions and shifted towards larger trainable…

A direct procedure for determining the propagator associated with a quantum mechanical problem was given by the Path Integration Procedure of Feynman. The Green function, which is the Fourier Transform with respect to the time variable of…

Quantum Physics · Physics 2008-04-25 Marcos Moshinsky , Emerson Sadurni , Adolfo del Campo

We develop fully noncommutative Feynman-Kac formulae by employing quantum stochastic processes. To this end we establish some theory for perturbing quantum stochastic flows on von Neumann algebras by multiplier cocycles. Multiplier cocycles…

Functional Analysis · Mathematics 2018-01-18 Alexander C. R. Belton , J. Martin Lindsay , Adam G. Skalski

A prominent goal of representation learning research is to achieve representations which are factorized in a useful manner with respect to the ground truth factors of variation. The fields of disentangled and equivariant representation…

Machine Learning · Computer Science 2023-09-26 Yue Song , T. Anderson Keller , Nicu Sebe , Max Welling

The predictions of the standard model of particle physics are highly successful in spite of the fact that several parts of the underlying quantum field theoretical framework are analytically problematic. Indeed, it has long been suggested,…

Mathematical Physics · Physics 2021-05-05 David M. Jackson , Achim Kempf , Alejandro H. Morales

Flow matching and diffusion bridge models have emerged as leading paradigms in generative speech enhancement, modeling stochastic processes between paired noisy and clean speech signals based on principles such as flow matching, score…

Audio and Speech Processing · Electrical Eng. & Systems 2026-02-23 Dahan Wang , Jun Gao , Tong Lei , Yuxiang Hu , Changbao Zhu , Kai Chen , Jing Lu

We introduce a new diagrammatic approach to perturbative quantum field theory, which we call flow-oriented perturbation theory (FOPT). Within it, Feynman graphs are replaced by strongly connected directed graphs (digraphs). FOPT is a…

High Energy Physics - Theory · Physics 2023-02-02 Michael Borinsky , Zeno Capatti , Eric Laenen , Alexandre Salas-Bernárdez

Starting from Feynman's Lagrangian description of quantum mechanics, we propose a method to construct explicitly the propagator for the Wigner distribution function of a single system. For general quadratic Lagrangians, only the classical…

Quantum Physics · Physics 2017-05-11 Dries Sels , Fons Brosens , Wim Magnus

The (Feynman) propagator $G(x_2,x_1)$ encodes the entire dynamics of a massive, free scalar field propagating in an arbitrary curved spacetime. The usual procedures for computing the propagator -- either as a time ordered correlator or from…

General Relativity and Quantum Cosmology · Physics 2021-04-19 T. Padmanabhan

Many interesting physical theories have analytic classical actions. We show how Feynman's path integral may be defined non-perturbatively, for such theories, without a Wick rotation to imaginary time. We start by introducing a class of…

High Energy Physics - Theory · Physics 2023-05-17 Job Feldbrugge , Neil Turok

We propose a new solvable class of multidimensional quantum harmonic oscillators for a linear diffusive particle and a quadratic energy absorbing well associated with a semi-definite positive matrix force. Under natural and easily checked…

Probability · Mathematics 2023-07-26 Pierre del Moral , Emma Horton

Work statistics characterizes important features of a non-equilibrium thermodynamic process. But the calculation of the work statistics in an arbitrary non-equilibrium process is usually a cumbersome task. In this work, we study the work…

Statistical Mechanics · Physics 2020-03-18 Tian Qiu , Zhaoyu Fei , Rui Pan , H. T. Quan