Title: Dynamically Reconfiguring Quantum Circuit Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum computing holds immense promise for revolutionizing various fields, but the design and optimization of quantum circuits remain a significant challenge. Current methods often rely on manual tweaking and are limited by the time and expertise required. This paper introduces a novel system for dynamically reconfiguring quantum circuit parameters during runtime, driven by reinforcement learning. Our approach addresses the bottleneck in quantum circuit optimization by automating the process and providing real-time adjustments based on performance metrics. The system is designed to intelligently explore parameter space, maximizing performance while ensuring stability and minimizing resource consumption. This work presents a framework for automated circuit optimization, paving the way for more efficient and accessible quantum computing.

DOI: 10.5281/zenodo.22264845

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