Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing
Researchers have developed a novel routing method for quantum circuits that incorporates calibration data to improve fidelity. This graph reinforcement learning approach uses same-day calibration information from IBM Heron processors to select hardware-edge SWAPs, outperforming standard routing methods like SABRE-best20 and target-aware SABRE in exact simulated fidelity. While the learned routing increases the number of routed two-qubit gates, it demonstrates a significant improvement in fidelity, particularly for smaller circuit families, suggesting a more robust compilation strategy for quantum processors. AI