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We consider a class of pseudodifferential evolution equations of the form $$u_t + (n(u) + Lu)_x = 0,$$ in which $L$ is a linear smoothing operator and $n$ is at least quadratic near the origin; this class includes in particular the Whitham…

Analysis of PDEs · Mathematics 2020-07-28 Mats Ehrnström , Mark D. Groves , Erik Wahlén

We present a rigorous framework to obtain evolution equations for the momentum distribution and higher order correlation functions in weakly interacting systems based on the Projection Operator Technique. These equations can be numerically…

Quantum Gases · Physics 2015-06-22 N. Nessi , A. Iucci

Optimal control theory is usually formulated as an indirect method requiring the solution of a two-point boundary value problem. Practically, the solution is obtained by iterative forward and backward propagation of quantum wavepackets.…

Quantum Physics · Physics 2020-10-09 Alejandro R. Ramos Ramos , Oliver Kühn

This work deals with optimal control problems as a strategy to drive bifurcating solution of nonlinear parametrized partial differential equations towards a desired branch. Indeed, for these governing equations, multiple solution…

Numerical Analysis · Mathematics 2023-08-08 Federico Pichi , Maria Strazzullo , Francesco Ballarin , Gianluigi Rozza

The iterative problem of solving nonlinear equations is studied. A new Newton like iterative method with adjustable parameters is designed based on the dynamic system theory. In order to avoid the derivative function in the iterative…

Numerical Analysis · Mathematics 2022-11-09 Yonglong Liao , Limin Cui

Unbounded potentials are always utilized to strictly confine quantum dynamics and generate bound or stationary states due to the existence of quantum tunneling. However, the existed accurate Wigner solvers are often designed for either…

Computational Physics · Physics 2019-06-04 Zhenzhu Chen , Yunfeng Xiong , Sihong Shao

Quantum computing offers a promising avenue for advancing computational methods in science and engineering. In this work, we introduce the quantum asymptotic numerical method (qANM), a framework for solving nonlinear problems using quantum…

The Bayesian learning rule is a natural-gradient variational inference method, which not only contains many existing learning algorithms as special cases but also enables the design of new algorithms. Unfortunately, when variational…

Machine Learning · Statistics 2020-10-27 Wu Lin , Mark Schmidt , Mohammad Emtiyaz Khan

Nonautonomous linear ordinary differential equations of the form $\dot{v}(t) = A(t)\, v(t)$, where $A(t)$ is non-skew-symmetric, are often used to describe nonunitary dynamics in a variety of fields that range from open quantum system…

Quantum Physics · Physics 2026-05-29 Pouya Khazaei , Eitan Geva

We develop a novel method for finding bifurcations for nonlinear systems of equations based on directly finding bifurcations through saddle points of extended quotients. The method is applied to find the saddle-node bifurcation point for…

Analysis of PDEs · Mathematics 2024-05-07 Yavdat Il'yasov

We prove the existence of solutions for an evolution quasi-variational inequality with a first order quasilinear operator and a variable convex set, which is characterized by a constraint on the absolute value of the gradient that depends…

Analysis of PDEs · Mathematics 2012-01-31 José Francisco Rodrigues , Lisa Santos

Linear equations play a pivotal role in many areas of science and engineering, making efficient solutions to linear systems highly desirable. The development of quantum algorithms for solving linear systems has been a significant…

Quantum Physics · Physics 2025-02-20 Nhat A. Nghiem

In this paper, we explore the embedding of nonlinear dynamical systems into linear ordinary differential equations (ODEs) via the Carleman linearization method. Under dissipative conditions, numerous previous works have established rigorous…

Quantum Physics · Physics 2025-02-03 Hsuan-Cheng Wu , Jingyao Wang , Xiantao Li

The paper examines the prominent algorithm D-MORPH to search for the optimal control of a quantum system in order to implement desired unitary evolution of the quantum system at the final time, and reveals new mathematical expressions for…

Quantum Physics · Physics 2017-09-19 Konstantin Zhdanov

We present efficient quantum algorithms for simulating time-dependent Hamiltonian evolution of general input states using an oracular model of a quantum computer. Our algorithms use either constant or adaptively chosen time steps and are…

Quantum Physics · Physics 2011-11-03 Nathan Wiebe , Dominic W. Berry , Peter Hoyer , Barry C. Sanders

We present a scaling technique which transforms the evolution problem for a nonlinear wave equation with small initial data to a linear wave equation with a distributional source. The exact solution of the latter uniformly approximates the…

Mathematical Physics · Physics 2011-03-23 Nikodem Szpak

Quantum Hamiltonians containing nonseparable products of non-commuting operators, such as $\hat{\bf x}^m \hat{\bf p}^n$, are problematic for numerical studies using split-operator techniques since such products cannot be represented as a…

Quantum Physics · Physics 2023-03-15 Maximilian Ciric , Denys I. Bondar , Ole Steuernagel

This paper presents an inverse optimality method to solve the Hamilton-Jacobi-Bellman equation for a class of nonlinear problems for which the cost is quadratic and the dynamics are affine in the input. The method is inverse optimal because…

Optimization and Control · Mathematics 2011-10-11 Luis Rodrigues , Didier Henrion , Mehdi Abedinpour Fallah

We introduce an iterative method to search for time-optimal Hamiltonians that drive a quantum system between two arbitrary, and in general mixed, quantum states. The method is based on the idea of progressively improving the efficiency of…

Quantum Physics · Physics 2019-12-25 Francesco Campaioli , William Sloan , Kavan Modi , Felix Alexander Pollock

A q-Gauss-Newton algorithm is an iterative procedure that solves nonlinear unconstrained optimization problems based on minimization of the sum squared errors of the objective function residuals. Main advantage of the algorithm is that it…

Optimization and Control · Mathematics 2021-05-28 Danijela Protic , Miomir Stankovic