FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement
This work addresses the challenge of noise in quantum computing for applications like quantum optimization and machine learning, representing an incremental advancement by directly optimizing fidelity rather than using proxy metrics.
The paper tackles the problem of improving quantum circuit reliability by maximizing process fidelity during the routing stage, introducing FIDDLE, a framework that uses a Gaussian Process surrogate model and reinforcement learning to outperform traditional methods based on indirect metrics.
Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in gate-based quantum computing is improving the reliability of quantum circuits, measured by process fidelity, during the transpilation process, particularly in the routing stage. In this paper, we address the Fidelity Maximization in Routing Stage (FMRS) problem by introducing FIDDLE, a novel learning framework comprising two modules: a Gaussian Process-based surrogate model to estimate process fidelity with limited training samples and a reinforcement learning module to optimize routing. Our approach is the first to directly maximize process fidelity, outperforming traditional methods that rely on indirect metrics such as circuit depth or gate count. We rigorously evaluate FIDDLE by comparing it with state-of-the-art fidelity estimation techniques and routing optimization methods. The results demonstrate that our proposed surrogate model is able to provide a better estimation on the process fidelity compared to existing learning techniques, and our end-to-end framework significantly improves the process fidelity of quantum circuits across various noise models.