Quantum Thermodynamics Inspired Heuristic Optimization Algorithm

Zhang, Jincheng

Published Dec 8, 2025• 0 citations

Abstract

This paper proposes a novel heuristic optimization algorithm that directly draws upon the core principles of quantum thermodynamics, including coherent evolution, thermal dissipation, quantum fluctuations, entropy generation, and the work-information coupling mechanism. Traditional optimization algorithms often get trapped in local optima when dealing with complex non-convex problems, lacking global exploration capabilities and dynamic diversity control. The proposed algorithm constructs a density matrix to represent the candidate solution set, maps the objective function to a dynamic energy level Hamiltonian, and achieves an organic combination of global exploration and local accelerated convergence through alternating updates using coherent evolution, thermal dissipation, quantum fluctuation operators, and work-information coupling operators. This paper elaborates on the algorithm's mathematical model, update mechanism, entropy-energy control strategy, and pseudocode implementation, providing a complete theoretical framework and a new approach to complex optimization problems.

DOI: 10.5281/zenodo.17852922

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