Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
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.
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