The Feynman path integral representation of quantum theory is used in a non–parametric Bayesian approach to determine quantum potentials from measurements on a canonical ensemble. This representation allows to study explicitly the classical and semiclassical limits and provides a unified description in terms of functional integrals: the Feynman path integral for the statistical operator and its derivative with respect to the potential, and the integration over the space of potentials for calculating the predictive density. The latter is treated in maximum a posteriori approximation, and various approximation schemes for the former are developed and discussed. A simple numerical example shows the applicability of the method
One of the main concepts in quantum physics is a density matrix, which is a symmetric positive defin...
Quantum probability is a subtle blend of quantum mechanics and classical probability theory. Its imp...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
We demonstrate how path integrals often used in problems of theoretical physics can be adapted to pr...
A new method is presented to reconstruct the potential of a quantum mechanical many-body system from...
We present a novel approach to the inference of spectral functions from Euclidean time correlator da...
The paper discusses the reconstruction of potentials for quantum systems at finite temperatures fro...
Using a new Bayesian method for solving inverse quantum problems, potentialsof quantum systems are r...
Quantum mechanics is basically a mathematical recipe on how to construct physical models. Historical...
Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observat...
Some methods for constructing uniform non-perturbative approximations of path integrals over a condi...
The Path-Integral Formulation of Quantum Mechanics is intro- duced along with a detailed mathematica...
The Monte Carlo Hamiltonian method developed recently allows to investigate ground state and low-lyi...
Machine learning models are usually trained by a large number of observations (big data) to make pr...
International audienceWe estimate the quantum state of a light beam from results of quantum homodyne...
One of the main concepts in quantum physics is a density matrix, which is a symmetric positive defin...
Quantum probability is a subtle blend of quantum mechanics and classical probability theory. Its imp...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
We demonstrate how path integrals often used in problems of theoretical physics can be adapted to pr...
A new method is presented to reconstruct the potential of a quantum mechanical many-body system from...
We present a novel approach to the inference of spectral functions from Euclidean time correlator da...
The paper discusses the reconstruction of potentials for quantum systems at finite temperatures fro...
Using a new Bayesian method for solving inverse quantum problems, potentialsof quantum systems are r...
Quantum mechanics is basically a mathematical recipe on how to construct physical models. Historical...
Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observat...
Some methods for constructing uniform non-perturbative approximations of path integrals over a condi...
The Path-Integral Formulation of Quantum Mechanics is intro- duced along with a detailed mathematica...
The Monte Carlo Hamiltonian method developed recently allows to investigate ground state and low-lyi...
Machine learning models are usually trained by a large number of observations (big data) to make pr...
International audienceWe estimate the quantum state of a light beam from results of quantum homodyne...
One of the main concepts in quantum physics is a density matrix, which is a symmetric positive defin...
Quantum probability is a subtle blend of quantum mechanics and classical probability theory. Its imp...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...