Brief

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

Researchers have developed a new diagnostic tool to analyze quantum Gaussian process kernels, revealing that seemingly unrelated issues in quantum machine learning are governed by the same underlying quantity: the normalized spectral entropy of the kernel Gram matrix. This diagnostic has been empirically validated across various kernel families and successfully transferred from simulators to IBM Heron hardware with low error rates. The findings suggest that the optimal kernel entropy depends on the target data, offering insights into improving Bayesian optimization in quantum machine learning. AI