基于动态量子势场的自适应优化算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum computing holds immense promise for revolutionizing various fields, and the development of efficient quantum algorithms is crucial for realizing this potential. However, the design and optimization of quantum circuits often involve complex parameter settings, which can be time-consuming and resource-intensive. This paper proposes a novel optimization algorithm based on dynamically adjusted quantum potentials. We introduce a method that automatically tunes the parameters of a quantum potential field, allowing the algorithm to adapt to the specific characteristics of the problem and improve both computational efficiency and accuracy. The core mechanism involves iteratively adjusting the potential field based on a feedback loop, leading to a more robust and adaptable optimization process. This work presents a new approach to quantum optimization that addresses limitations of traditional methods.

DOI: 10.5281/zenodo.22296742

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