Reinforcement learning using AlphaZero to optimize entanglement sythesis in circuit QED

We present an implementation of a reinforcement learning algorithm using deep neural networks and Monte Carlo tree search to demonstrate global optimization of the control landscape of superconducting qubits. Our findings show significant improvement in time and best-found fidelity compared to long established local-climbing methods such as GRAPE. Our research suggests that for increasingly complex quantum control problems (as system sizes and architectures grow), sophisticated machine learning may become a preferable tool for calibrating and optimizing high-performance quantum computing experiments.