基于自适应的量子加速器优化算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum acceleration, the ability to significantly increase the speed and efficiency of quantum computations, remains a major challenge in the field of quantum computing. Traditional optimization algorithms often struggle to effectively address the complexities of quantum system evolution, limiting the potential for substantial acceleration. This paper proposes a novel optimization algorithm, termed "Adaptive Quantum Parameter Adjustment" (AQPA), that dynamically adjusts quantum system parameters based on the system's inherent characteristics. We demonstrate the efficacy of AQPA through a series of simulations and analysis, showcasing a substantial improvement in quantum acceleration compared to existing optimization techniques. The core mechanism of AQPA hinges on a self-adaptive parameter tuning process, ensuring a robust and efficient optimization strategy. This paper introduces a framework for dynamically adjusting quantum parameters, offering a potential pathway toward more effective quantum acceleration.

DOI: 10.5281/zenodo.22301067

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