Title: Adaptive Constraint Optimization via Quantum Annealing (ACOQ)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces Adaptive Constraint Optimization via Quantum Annealing (ACOQ), a novel optimization algorithm designed to address the limitations of traditional methods. Traditional optimization techniques often rely on static constraints, which can be suboptimal in dynamic environments. ACOQ leverages quantum annealing to dynamically adjust constraint constraints based on the system's response to the optimization process, offering a significant advantage in scenarios requiring real-time adaptation. This paper details the algorithm's architecture, the quantum annealing process, the adaptive constraint relaxation mechanism, and a comprehensive evaluation of its performance across a range of test cases. We demonstrate that ACOQ provides a substantial improvement in solution quality and computational efficiency compared to existing methods, particularly in problems exhibiting complex, dynamic constraints. The implementation showcases a successful application to a constrained optimization problem, illustrating the algorithm's potential for wider adoption in various fields.

DOI: 10.5281/zenodo.22478945

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