基于自适应的量子态的自适应几何优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum systems often face challenges in achieving optimal performance due to the inherent complexity of their parameters. Traditional optimization techniques may struggle with the non-linearity and high dimensionality of these parameters, leading to suboptimal results. This paper introduces a novel approach to quantum system optimization – a self-adaptive geometric optimization method. This method dynamically adjusts the quantum state parameters through an iterative self-optimization process, leveraging the inherent properties of quantum mechanics to enhance system performance. We demonstrate the effectiveness of this approach through theoretical analysis and experimental validation on a set of benchmark quantum systems. The core mechanism centers around a self-adjusting optimization loop that iteratively refines the quantum state. The resulting system exhibits improved performance in terms of coherence and fidelity. This work presents a significant advancement in quantum optimization, offering a robust and adaptable solution for complex quantum system design.

DOI: 10.5281/zenodo.22184608

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