Quantum State Adaptive Geometry Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum state adaptive geometry optimization algorithm designed to address complex constraint optimization problems, particularly in scenarios with sparse data. Traditional constraint optimization methods struggle with intricate geometric constraints. Leveraging quantum state optimization, this algorithm constructs a system based on adaptive geometric models, employing simulation feedback to iteratively refine the solution and achieve optimal results. This approach offers a significant advancement in the field, potentially providing more efficient and effective solutions for a broader range of constraints. The core mechanism centers around dynamically adjusting geometric constraints through quantum state manipulation, allowing the algorithm to effectively navigate and optimize complex geometries.

DOI: 10.5281/zenodo.22204829

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