Quantum-Driven Constraint Optimization for Algorithmic Diversity

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-driven constraint optimization presents a novel approach to algorithmic diversity by leveraging quantum annealing or quantum approximate optimization algorithms (QAOA) to systematically explore the space of possible algorithmic solutions. This paper introduces a framework where quantum computation is utilized to iteratively refine algorithmic designs, promoting the discovery of novel and diverse solutions that might be missed by classical optimization techniques. The core mechanism centers on the ability of quantum algorithms to escape local optima and navigate complex search spaces, ultimately leading to a more comprehensive exploration of algorithmic possibilities. This work addresses the limitations of conventional optimization methods by introducing a quantum-enhanced search strategy, ultimately aiming to significantly enhance the diversity of algorithmic designs.

DOI: 10.5281/zenodo.22509711

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