自适应拓扑量子计算的自适应几何拓扑结构优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the application of adaptive geometric topology optimization to self-adaptive quantum computation. Traditional quantum algorithms often rely on static mappings, limiting their capacity to handle complex geometric structures. We propose a novel algorithm that leverages the dynamic evolution of quantum states, driven by learned geometric patterns, to achieve enhanced computational efficiency. The core mechanism centers around a self-optimizing algorithm that analyzes the quantum state's geometry and adjusts its parameters to optimize performance. This approach addresses limitations of static optimization strategies by introducing a continuous, adaptive refinement process. The potential benefits include improved adaptability, enhanced control over quantum states, and potentially, higher computational throughput. This work presents a framework for dynamically sculpting the quantum state, offering a new pathway towards more efficient quantum computation.

DOI: 10.5281/zenodo.22495998

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