Quantum-Inspired Optimization for Non-Convex Functions

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-inspired optimization represents a paradigm shift in the field of optimization, aiming to accelerate the convergence of algorithms for complex, non-convex functions. This paper explores the application of quantum mechanics principles to model objective functions and leverage quantum algorithms to achieve enhanced performance. We present a novel quantum-inspired optimization technique, demonstrating its potential to overcome the limitations of traditional methods in solving challenging optimization problems. The core mechanism hinges on a sophisticated approach to representing the objective function as a quantum system, utilizing quantum algorithms to approximate solutions and thereby accelerate convergence. The research highlights the key advantages of this approach, particularly in tackling non-convex functions, and discusses future directions for research and development.

DOI: 10.5281/zenodo.22278686

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