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Low-dimensional Quantum Material-inspired Quantum Optimization Algorithm

Zhang, Jincheng

Published Nov 8, 2025• 0 citations
Publisher

Abstract

Low-dimensional quantum materials (LDQM-QO) possess unique properties, including quantum confinement, energy level discretization, spin-orbit coupling, and topological edge state protection, and hold great research value in quantum information and nanodevice research. This paper proposes a novel optimization algorithm, the Low-Dimensional Quantum Materials-Inspired Quantum Optimization (LDQM-QO). Its core concept is to map the solution space of the optimization problem into the electronic quantum states of the low-dimensional quantum material. It then draws on the mechanisms of quantum fluctuations, quantum tunneling, spin-orbit coupling, and topological edge states to design an iterative update method. The algorithm mathematically incorporates energy level mapping functions, quantum fluctuation perturbations, spin coupling collaboration, and topological edge state navigation mechanisms, resulting in a completely original optimization strategy that effectively enhances global search capabilities and avoids local optimality traps. This paper deduces the algorithm's mathematical formulation in detail, demonstrating the theoretical basis and operating principles of each mechanism, providing a new methodology for the intersection of low-dimensional quantum materials theory and heuristic optimization.

DOI
10.5281/zenodo.17557746

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