基于自适应的量子态的优化策略

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum computing holds immense promise for solving complex problems currently intractable for classical computers. However, realizing this potential necessitates significant advancements in quantum algorithm design and optimization. This paper explores a novel optimization strategy based on the inherent adaptability of quantum states. We propose a framework that dynamically adjusts quantum states to minimize error rates and maximize fidelity during the execution of quantum algorithms. The core mechanism involves a feedback loop that continuously refines the quantum state based on the algorithm's progress, effectively guiding the system towards optimal performance. This approach offers a promising pathway towards enhanced quantum computing efficiency and reduced error rates. The study demonstrates the efficacy of this method through simulations and preliminary experimental results, showcasing the potential for significant performance improvements.

DOI: 10.5281/zenodo.22203199

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