自适应非线性量子计算的几何优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the application of a novel geometric optimization algorithm, termed "Self-Adaptive Nonlinear Quantum Computation" (SANQC), to address complex geometrical problems. Traditional optimization methods often struggle with high dimensionality and non-linear constraints. SANQC leverages quantum entanglement to create a dynamic and adaptable approach to shape optimization, significantly enhancing the efficiency of quantum computation. The paper details the algorithm's core mechanics, highlighting the utilization of quantum entanglement to explore multiple solution spaces concurrently. We demonstrate the effectiveness of SANQC through a series of illustrative examples, showcasing its ability to outperform conventional algorithms in specific geometric optimization tasks. This work presents a promising framework for accelerating the development and application of quantum algorithms in areas demanding intricate geometric manipulation.

DOI: 10.5281/zenodo.22489289

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