Quantum-Inspired Algorithms for Optimization of Large-Scale Combinatorial Problems

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper investigates the application of quantum-inspired algorithms to the optimization of large-scale combinatorial problems. Recognizing the limitations of current quantum computing hardware, this research focuses on developing classical algorithms that emulate key quantum principles – superposition and interference – to achieve significant performance improvements over traditional optimization methods. The core idea is to leverage amplitude amplification and interference cancellation techniques, inspired by quantum mechanics, to efficiently explore the solution space. We present a framework for designing such algorithms, detailing the mathematical formulations and the core mechanisms involved. The research aims to demonstrate that, without the need for actual quantum computers, these quantum-inspired approaches can offer a compelling alternative for tackling complex optimization challenges. The presented algorithms are evaluated conceptually, focusing on the mathematical foundations and design principles rather than empirical testing.

DOI: 10.5281/zenodo.22140458

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