Title: Quantum-Inspired Lattice Optimization - Adaptive Topology Generation

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Inspired Lattice Optimization aims to revolutionize computational modeling and data representation by leveraging the principles of quantum mechanics to discover novel and highly efficient lattice configurations. This paper introduces a novel optimization technique, termed "Quantum-Inspired Lattice Mapping," which mimics quantum tunneling and superposition in lattice structures, enabling automated topology design. The core mechanism involves generating optimized topologies through a self-learning process guided by the quantum-inspired algorithm. We explore the potential of this approach to overcome limitations of traditional lattice optimization methods, offering a pathway to more effective data representation and computational modeling. This work presents a robust framework for adapting to diverse problem domains, showcasing its effectiveness through theoretical analysis and preliminary experimental validation. The focus is on generating topologies with enhanced computational efficiency and tailored properties for specific applications.

DOI: 10.5281/zenodo.22277821

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