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Dynamic Quantum Topology Adaptive Topology Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations
Publisher

Abstract

This paper explores the application of dynamic quantum topology optimization to enhance the design of quantum systems, particularly focusing on the optimization of quantum crystals and circuits. Traditional approaches to topology optimization rely on manual parameter adjustments, limiting flexibility and potentially hindering the discovery of novel solutions. We introduce a novel algorithm that dynamically adjusts the quantum topology structure during the optimization process, enabling a more intuitive and powerful approach to finding optimal configurations. The core mechanism involves iteratively refining the quantum topology through a feedback loop, leveraging the inherent properties of quantum systems to guide the optimization toward desired outcomes. This approach promises significant improvements in the design of complex quantum devices and circuits.

DOI
10.5281/zenodo.22290161

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This paper investigates the application of dynamic quantum topology to optimize quantum algorithms, focusing on quantum machine learning and quantum optimization. The core concept is to dynamically adjust the quantum topology structure of a quantum system, enabling the algorithm to explore different solutions more effectively. We propose a novel algorithm that iteratively refines the quantum state through a series of topological transformations, leveraging the inherent properties of quantum topology to guide the optimization process. This approach offers a promising avenue for enhancing the performance of quantum algorithms in complex scenarios. The paper explores the potential benefits of this dynamic topology approach, including improved convergence rates, enhanced solution space exploration, and adaptability to varying problem characteristics. We present a preliminary implementation demonstrating the efficacy of this method on a simplified quantum optimization problem.
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