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This work introduces novel numerical algorithms for computational quantum mechanics, grounded in a representation of the Laplace operator -- frequently used to model kinetic energy in quantum systems -- via the heat semigroup. The key…

Quantum Physics · Physics 2025-01-16 Evgueni Dinvay

The quantum-mechanical solution to the problem of radiative recombination of an electron in a Coulomb field, obtained in the approximation of the smallness of the electron coupling with the radiation field, has been known for a long time.…

Cosmology and Nongalactic Astrophysics · Physics 2022-12-14 Barabanov A. L. , Belotsky K. M. , Esipova E. A. , Kalashnikov D. S. , Letunov A. Yu

The problem of establishing out-of-sample bounds for the values of an unkonwn ground-truth function is considered. Kernels and their associated Hilbert spaces are the main formalism employed herein along with an observational model where…

Machine Learning · Computer Science 2022-09-13 Paul Scharnhorst , Emilio T. Maddalena , Yuning Jiang , Colin N. Jones

In this contribution, kernel approximations are applied as ansatz functions within the Deep Ritz method. This allows to approximate weak solutions of elliptic partial differential equations with weak enforcement of boundary conditions using…

Numerical Analysis · Mathematics 2024-10-07 Hendrik Kleikamp , Tizian Wenzel

I discuss the role that relativistic considerations play in quantum information processing. First I describe how the causality requirements limit possible multi-partite measurements. Then the Lorentz transformations of quantum states are…

Quantum Physics · Physics 2007-05-23 Daniel R. Terno

Quantum kernel method is one of the key approaches to quantum machine learning, which has the advantages that it does not require optimization and has theoretical simplicity. By virtue of these properties, several experimental…

Quantum Physics · Physics 2022-11-28 Norihito Shirai , Kenji Kubo , Kosuke Mitarai , Keisuke Fujii

The study of quantum chromodynamics (QCD) over the past quarter century has had relatively little impact on the traditional approach to the low-energy nuclear many-body problem. Recent developments are changing this situation. New…

Nuclear Theory · Physics 2009-11-07 R. J. Furnstahl

The development of the relativistic all-order method where all single, double, and partial triple excitations of the Dirac-Hartree-Fock wave function are included to all orders of perturbation theory led to many important results for study…

Atomic Physics · Physics 2015-05-27 H. Gharibnejad , E. Eliav , M. S. Safronova , A. Derevianko

This work is concerned with multi-dimensional integrals, which are making their appearance in few-body atomic and nuclear physics. It is shown that the relevant two- and three-dimensional integrals can be reduced to one-dimensional form.…

Mathematical Physics · Physics 2010-03-02 E. Z. Liverts , N. Barnea

Weak-interaction currents are studied in a recently proposed effective field theory of the nuclear many-body problem. The Lorentz-invariant effective field theory contains nucleons, pions, isoscalar scalar ($\sigma$) and vector ($\omega$)…

Nuclear Theory · Physics 2009-11-07 Sergei M. Ananyan , Brian D. Serot , John Dirk Walecka

The vector form of a Lorentz transformation which is separated with time and space parts is studied. It is necessary to introduce a new definition of the relative velocity in this transformation, which plays an important role for the…

Nuclear Theory · Physics 2007-05-23 Yongkyu Ko

The apparent impossibility of extending non-relativistic quantum mechanics to a relativistic quantum theory is shown to be due to the insufficient structural richness of the field of complex numbers over which quantum mechanics is built. A…

General Physics · Physics 2012-04-17 Emile Grgin

Support vector machines and kernel methods have recently gained considerable attention in chemoinformatics. They offer generally good performance for problems of supervised classification or regression, and provide a flexible and…

Quantitative Methods · Quantitative Biology 2007-08-02 Pierre Mahé , Jean-Philippe Vert

The Laplace-Beltrami problem on closed surfaces embedded in three dimensions arises in many areas of physics, including molecular dynamics (surface diffusion), electromagnetics (harmonic vector fields), and fluid dynamics (vesicle…

Numerical Analysis · Mathematics 2023-06-21 Tristan Goodwill , Michael O'Neil

An overview on the relativistic Dirac-Brueckner approach to the nuclear many-body problem is given. Different approximation schemes are discussed, with particular emphasis on the nuclear self-energy and the saturation mechanism of nuclear…

Nuclear Theory · Physics 2017-01-25 Christian Fuchs

Accurate simulations of atomistic systems from first principles are limited by computational cost. In high-throughput settings, machine learning can reduce these costs significantly by accurately interpolating between reference…

Chemical Physics · Physics 2022-11-28 Haoyan Huo , Matthias Rupp

The use of kernels for nonlinear prediction is widespread in machine learning. They have been popularized in support vector machines and used in kernel ridge regression, amongst others. Kernel methods share three aspects. First, instead of…

Machine Learning · Statistics 2025-08-25 Patrick J. F. Groenen , Michael Greenacre

In the present paper authors introduce the L_n-integral transform and the inverse integral transform for n = 2^k, k=0,1,2,..., as a generalization of the classical Laplace transform and the inverse Laplace transform, respectively.…

Classical Analysis and ODEs · Mathematics 2014-03-11 Nese Dernek , Fatih Aylikci

In recent years many-body perturbation theory encountered a renaissance in the field of ab initio nuclear structure theory. In various applications it was shown that perturbation theory, including novel flavors of it, constitutes a useful…

Nuclear Theory · Physics 2020-01-29 Alexander Tichai , Robert Roth , Thomas Duguet

Identify, Transform, Benchmark, Show Quantum Advantage (ITBQ): Evaluating use cases for quantum computers. We introduce a four-step framework for assessing quantum computing applications -- from identifying relevant industry problems to…

Quantum Physics · Physics 2025-10-07 Michael Marthaler , Peter Pinski , Vladimir Rybkin , Iris Schwenk , Pascal Stadler , Marina Walt