Quantum-Inspired Algorithms for Large-Scale Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores the potential of quantum-inspired algorithms for tackling large-scale optimization problems. Traditional optimization methods often struggle with the computational complexity of problems involving numerous variables and constraints. Quantum mechanics, particularly concepts like superposition and entanglement, offers a radically different approach to problem-solving. This work investigates how these concepts can be translated into algorithmic designs, aiming to improve the efficiency and speed of optimization processes. We propose a framework for developing algorithms that mimic quantum phenomena, focusing on techniques that enable exploration of the solution space in a more parallel and potentially faster manner. The core of this approach lies in leveraging mathematical structures derived from quantum mechanics to guide search strategies. We outline key algorithmic ideas, emphasizing the potential for improved convergence rates and scalability. The presented concepts represent a novel application of quantum-inspired ideas to the field of optimization, offering a promising avenue for addressing complex real-world problems.

DOI: 10.5281/zenodo.22636895

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