Quantum State Adaptive Geometry Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum state adaptive geometry optimization algorithm designed to tackle high-dimensional optimization problems. We propose a quantum mechanism leveraging quantum entanglement and superposition to explore the optimization landscape, enabling the discovery of global optima and efficient handling of complex scenarios. The core of our approach lies in the construction of a quantum state representation that effectively captures and manipulates the solution space, facilitating the exploration of the optimization function. This work demonstrates the potential of quantum optimization for a diverse range of problems, offering a fundamentally different approach compared to classical algorithms.

DOI: 10.5281/zenodo.22249023

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