Title: Adaptive Constraint Satisfaction with Quantum Simulation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents an innovative approach to Adaptive Constraint Satisfaction (ACS) utilizing quantum simulation to dynamically optimize constraint combinations. The core claim centers on the creation of an algorithm that adapts constraint constraints based on a quantum simulation of a system, thereby enabling rapid identification of optimal solutions through quantum tunneling. This method promises to significantly accelerate the search process compared to traditional, static constraint satisfaction techniques. The paper details the algorithm's architecture, quantum annealing implementation, and the rationale behind its design. We explore the potential of quantum simulation to proactively refine constraint sets, leading to enhanced emergent behavior within the system being modeled. This research contributes to the advancement of adaptive optimization algorithms and offers a novel framework for tackling complex constraint satisfaction problems.

DOI: 10.5281/zenodo.22194066

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