Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:quant-ph/0105142 · Annals of Physics

A Convergent Iterative Solution of the Quantum Double-Well Potential

R. Friedberg, T.D. Lee, W.Q. Zhao, A. Cimenser

Published Nov 1, 2001• 14 citations
arXivHTMLPDF
DOI
10.1006/aphy.2001.6187

Related papers

Convergent iterative solutions of Schroedinger equation for a generalized double well potential

R. Friedberg, T.D. Lee, W.Q. Zhao

arXiv:0709.1997•Annals of Physics•Oct 1, 2007•2 citations

Convergent Iterative Solutions of Schroedinger Equation for a Generalized Double Well Potential ∗

Richard M. Friedberg, T. D. Lee, W. Q. Zhao

We present an explicit convergent iterative solution for the lowest energy state of the Schroedinger equation with a generalized double well potential V = g2 2 (x2 − 1) 2 (x 2 + a). The condition for the convergence of the iteration procedure and the dependence of the shape of the groundstate wave function on the parameter a are discussed.

•Jan 1, 2008•3 citations

SOLUTION OF SCHRODINGER EQUATION FOR A DOUBLE -WELL POTENTIAL

Boguslaw Stec, Czesław Jędrzejek

Acta Physica Polonica A•Jan 1, 1986•0 citations

Iterative Solution for Generalized Sombrero-shaped Potential in $N$-dimensional Space

Wei‐Guang Zhao

An explicit convergent iterative solution for the lowest energy state of the Schroedinger equation with generalized $N$-dimensional Sombrero-shaped potential is presented. The condition for the convergence of the iteration procedure and the dependence of the shape of the groundstate wave function on the parameters are discussed.

arXiv:0712.4054•Dec 25, 2007•0 citations

Improving the renormalization group approach to the quantum-mechanical double well potential

Dario Zappalà

arXiv:quant-ph/0108019•Physics Letters A•Nov 1, 2001•59 citations

Quantum-Inspired Iterative Optimization

Jincheng Zhang

This paper presents a novel framework for iterative optimization that draws inspiration from quantum-inspired algorithms. Traditional optimization methods often struggle with convergence issues, particularly in complex, high-dimensional spaces. This work introduces a quantum-inspired iterative approach, utilizing a novel process of candidate generation and evaluation to accelerate convergence and improve solution quality. We demonstrate the effectiveness of this framework through a series of simulations and analysis, highlighting its potential to overcome limitations of conventional optimization techniques. The core mechanism centers around employing quantum-mechanical concepts to guide the exploration of potential solutions, thereby fostering a more efficient and robust optimization process. This research explores the potential for leveraging quantum-inspired algorithms to achieve superior performance compared to existing methods, specifically targeting scenarios with complex landscapes and high-dimensional parameter spaces.

•Aug 31, 2026•0 citations
Iterative Solution for Generalized Sombrero-shaped Potential in NNN-dimensional Space
An explicit convergent iterative solution for the lowest energy state of the Schroedinger equation with generalized NNN-dimensional Sombrero-shaped potential is presented. The condition for the convergence of the iteration procedure and the dependence of the shape of the groundstate wave function on the parameters are discussed.
This paper presents a novel framework for iterative optimization that draws inspiration from quantum-inspired algorithms. Traditional optimization methods often struggle with convergence issues, particularly in complex, high-dimensional spaces. This work introduces a quantum-inspired iterative approach, utilizing a novel process of candidate generation and evaluation to accelerate convergence and improve solution quality. We demonstrate the effectiveness of this framework through a series of simulations and analysis, highlighting its potential to overcome limitations of conventional optimization techniques. The core mechanism centers around employing quantum-mechanical concepts to guide the exploration of potential solutions, thereby fostering a more efficient and robust optimization process. This research explores the potential for leveraging quantum-inspired algorithms to achieve superior performance compared to existing methods, specifically targeting scenarios with complex landscapes and high-dimensional parameter spaces.