Machine Learning for Quantum Circuit Optimization
Researchers have developed a machine learning model to rank logically equivalent quantum circuits, optimizing for hardware performance. This method addresses the significant variations in fidelity observed across different qubit assignments on real quantum hardware. The model, trained on hardware execution data, incorporates contextual device noise without requiring extensive tomography, ultimately outperforming conventional layout selection strategies.