Adaptive Quantum Circuit Design – Optimizing for Circuit Length and Fidelity

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum circuit design presents a significant challenge due to the inherent complexity of quantum systems and the need to balance circuit length with fidelity. Traditional methods often rely on fixed parameter settings, limiting the potential for substantial improvements. This paper introduces an adaptive quantum circuit design algorithm leveraging reinforcement learning to dynamically adjust circuit parameters during synthesis, optimizing for both length and fidelity. We demonstrate the effectiveness of this approach through a comprehensive analysis of circuit designs and performance metrics, highlighting its potential to significantly enhance both circuit length and overall quality. The core mechanism centers around a reinforcement learning agent that learns to optimize circuit parameters based on the current state of the circuit, leading to more efficient and robust designs.

DOI: 10.5281/zenodo.22193549

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