Universal Quantum Control through Deep Reinforcement Learning

Understand

Emerging reinforcement learning techniques using deep neural networks have shown great promise in control optimization.

• They harness non-local regularities of noisy control trajectories and facilitate transfer learning between tasks.
• To leverage these powerful capabilities for quantum control optimization, we propose a new control framework to simultaneously optimize the speed and fidelity of quantum computation against both leakage and stochastic control errors.
• For a broad family of two-qubit unitary gates that are important for quantum simulation of many-electron systems, we improve the control robustness by adding control noise into training environments for reinforcement learning agents trained with trusted-region-policy-optimization.