Self-Adaptive Topological Geometry for Quantum Computing Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper explores the application of self-adaptive geometric topology to quantum computing optimization. Traditional optimization techniques often struggle with complex, non-convex landscapes, while topological optimization offers a promising alternative. We propose a novel method that leverages the inherent properties of geometric topology to guide the search for optimal quantum circuit parameters. This approach allows for the exploration of a larger parameter space, potentially leading to significant efficiency gains. The core mechanism involves constructing and manipulating topological structures that provide a simplified representation of the problem, enabling faster convergence and improved solution quality. The research investigates the effectiveness of this method through simulations and preliminary analysis of quantum computation tasks. This work contributes to the development of more efficient and robust quantum algorithms.

DOI: 10.5281/zenodo.22292427

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