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Machine learning has become a premier tool in physics and other fields of science. It has been shown that the quantum mechanical scattering problem can not only be solved with such techniques, but it was argued that the underlying neural…

Computational Physics · Physics 2021-02-08 Bastian Kaspschak , Ulf-G. Meißner

Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering. Operator learning provides data-driven surrogates that map between function spaces. However, instead of full-field…

Machine Learning · Computer Science 2024-12-31 Daniel Zhengyu Huang , Nicholas H. Nelsen , Margaret Trautner

Operator-valued frames (or g-frames) are generalizations of frames and fusion frames and have been used in packets encoding, quantum computing, theory of coherent states and more. In this paper, we give a new formula for operator-valued…

Functional Analysis · Mathematics 2015-04-27 L. Gavruta , P. Gavruta

By employing non-equispaced grid points near boundaries, boundary-optimized upwind finite-difference operators of orders up to nine are developed. The boundary closures are constructed within a diagonal-norm summation-by-parts (SBP)…

Numerical Analysis · Mathematics 2026-02-06 Ken Mattsson , David Niemelä , Andrew R. Winters

We give a group-theoretic interpretation of non-relativistic holography as equivalence between representations of the Schrodinger algebra describing bulk fields and boundary fields. Our main result is the explicit construction of the…

High Energy Physics - Theory · Physics 2010-01-06 N. Aizawa , V. K. Dobrev

The spinor representation of the quantum group $U_q(su(N))$ is given in terms of a set of fermion creation and annihilation operators. It is shown that the $q$-fermion operators introduced earlier can be identifi ed with the conventional…

q-alg · Mathematics 2009-10-30 Minoru Hirayama , Shiori Kamibayashi

Linear response theory is concerned with the way in which a physical system reacts to a small change in the applied forces. Here we show that quantum mechanics in the Heisenberg representation can be understood as a linear response theory.…

Quantum Physics · Physics 2025-04-07 Ana María Cetto , Luis de la Peña

We develop a theoretical analysis for special neural network architectures, termed operator recurrent neural networks, for approximating nonlinear functions whose inputs are linear operators. Such functions commonly arise in solution…

Optimization and Control · Mathematics 2022-01-05 Maarten V. de Hoop , Matti Lassas , Christopher A. Wong

In this paper we develop the global symbolic calculus of pseudo-differential operators generated by a boundary value problem for a given (not necessarily self-adjoint or elliptic) differential operator. For this, we also establish elements…

Analysis of PDEs · Mathematics 2016-10-10 Michael Ruzhansky , Niyaz Tokmagambetov

Quantum computers promise to revolutionise electronic simulations by overcoming the exponential scaling of many-electron problems. While electronic wave functions can be represented using a product of fermionic unitary operators, shallow…

Quantum Physics · Physics 2022-07-04 Hugh G. A. Burton , Daniel Marti-Dafcik , David P. Tew , David J. Wales

We introduce the characteristic operator as the generalization of the usual concept of a transfer function of linear input-plant-output systems to arbitrary quantum nonlinear Markovian input-output models. This is intended as a tool in the…

Quantum Physics · Physics 2015-01-21 J. E. Gough

Vertex operator approach is a powerful method to study exactly solvable models. We review recent progress of vertex operator approach to semi-infinite spin chain. (1) The first progress is a generalization of boundary condition. We study…

Exactly Solvable and Integrable Systems · Physics 2019-02-13 Takeo Kojima

A quantum processor is a device with a data register and a program register. The input to the program register determines the operation, which is a completely positive linear map, that will be performed on the state in the data register. We…

Quantum Physics · Physics 2009-11-07 Mark Hillery , Mario Ziman , Vladimir Buzek

The core of quantum tomography is the possibility of writing a generally unbounded complex operator in form of an expansion over operators that are generally nonlinear functions of a generally continuous set of spectral densities--the…

Quantum Physics · Physics 2015-05-13 G. M. D'Ariano , M. F. Sacchi

We generalize the very well known boundary operator of the ordinary singular homology theory, defined in many books about algebraic topology. We describe a variant of this ordinary simplicial boundary operator where the usual boundary…

Geometric Topology · Mathematics 2011-09-13 Volker W. Thürey

Operator products occur naturally in a range of regularized boundary integral equation formulations. However, while a Galerkin discretisation only depends on the domain space and the test (or dual) space of the operator, products require a…

Numerical Analysis · Mathematics 2017-11-30 Timo Betcke , Matthew Scroggs , Wojciech Smigaj

Based on empirical evidence, quantum systems appear to be strictly linear and gauge invariant. This work uses concise mathematics to show that quantum eigenvalue equations on a one dimensional ring can either be gauge invariant or have a…

Quantum Physics · Physics 2014-07-15 Arthur Davidson

Nonlinear dynamical systems with symmetries exhibit a rich variety of behaviors, including complex attractor-basin portraits and enhanced and suppressed bifurcations. Symmetry arguments provide a way to study these collective behaviors and…

Dynamical Systems · Mathematics 2019-10-23 Anastasiya Salova , Jeffrey Emenheiser , Adam Rupe , James P. Crutchfield , Raissa M. D'Souza

The Hamiltonian of the harmonic oscillator is usually defined as a differential operator, but an integral representation can be obtained by using the coherent state quantization. The finite frame quantization is a finite counterpart of the…

Mathematical Physics · Physics 2013-08-27 Nicolae Cotfas , Daniela Dragoman

This paper proposes a brain-inspired approach to quantum machine learning with the goal of circumventing many of the complications of other approaches. The fact that quantum processes are unitary presents both opportunities and challenges.…

Machine Learning · Computer Science 2019-05-16 Bruce MacLennan
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