基于动态图灵机的自适应量子电路优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of a dynamically-tuned Turing Machine (DTT) framework for quantum circuit optimization. Traditional quantum circuit optimization often relies on fixed parameters, limiting the algorithm's ability to adapt to subtle variations in circuit behavior. We propose a novel algorithm that utilizes a DTT to dynamically adjust circuit parameters, enabling a more sophisticated and accurate optimization process. The core mechanism involves the evolution of a state space driven by the quantum circuit's properties, allowing for continuous parameter refinement. This approach promises improved precision and adaptability compared to static optimization methods. We detail the algorithm's design, implementation, and preliminary results demonstrating its effectiveness in optimizing specific quantum circuit examples.

DOI: 10.5281/zenodo.22267340

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