From Noise Modeling to Layout Optimization: A Framework for Quantum Circuit Fidelity Enhancement With Machine Learning

ABSTRACT Executing quantum circuits (QCs) on noisy intermediate‐scale quantum (NISQ) devices faces significant challenges due to quantum noise that impacts computational reliability. Layout selection presents a promising approach to enhance execution quality by mapping logical qubits to high‐fidelity physical qubits. Nevertheless, traditional approaches struggle to find high‐fidelity layouts mainly because of the difficulties in modeling noise characteristics. In this work, a machine learning‐driven layout optimization framework is introduced. By developing a novel feature set that considers information of circuit structures, backend calibration data and related factors, the trained machine learning (ML) model can accurately predict the fidelity of QCs under different candidate layouts, facilitating selecting high‐fidelity layouts. By evaluating proposed framework on tianyan176‐2 quantum computer using 6486 random QCs and 10 real‐world quantum programs, the results provide three key insights: first, the random forest model demonstrates superior noise modeling capabilities compared to the traditional product model and other ML models; second, the proposed framework significantly improves QC execution fidelity compared with random layout selection and Mapomatic ; furthermore, the SHapley Additive exPlanations (SHAP) analysis is conducted, which reveals strong correlations between features likes liveness and depth with QC execution fidelity, thereby facilitating understanding the random forest model's decision‐making mechanism in layout optimization.