Adaptive Quantum Computing for Nonlinear Constraint Solving

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel approach to nonlinear constraint solving leveraging the unique capabilities of quantum computing. Traditional methods often rely on trial-and-error, limiting the potential for efficient solutions. We propose a quantum-enhanced constraint solving framework that dynamically adjusts parameters to achieve optimal results. The core mechanism involves the quantum computation of constraint satisfaction, allowing for the discovery of underlying patterns and rules governing the problem's solution. This work demonstrates the effectiveness of the proposed method through a series of simulations and analyses, suggesting significant improvements in constraint solving efficiency and potential for addressing complex, non-trivial constraints. We highlight the advantages of quantum computation in this domain and outline future research directions.

DOI: 10.5281/zenodo.22191033

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