Universal quantum control through deep reinforcement learning

• Published in: npj quantum information Vol. 5; no. 1
• Main Authors: Niu, Murphy Yuezhen, Boixo, Sergio, Smelyanskiy, Vadim N., Neven, Hartmut
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23-04-2019; Nature Publishing Group
• Subjects: 639/766/483/2802; 639/766/483/481; Classical and Quantum Gravitation; Fidelity; Learning algorithms; Neural networks; Optimization; Physics; Physics and Astronomy; Quantum Computing; Quantum Field Theories; Quantum Information Technology; Quantum Physics; Reinforcement; Relativity Theory; Spintronics; Stochasticity; String Theory; Transfer learning
• ISSN: 2056-6387; 2056-6387
• DOI: 10.1038/s41534-019-0141-3

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. The agent control solutions demonstrate a two-order-of-magnitude reduction in average-gate-error over baseline stochastic-gradient-descent solutions and up to a one-order-of-magnitude reduction in gate time from optimal gate synthesis counterparts. These significant improvements in both fidelity and runtime are achieved by combining new physical understandings and state-of-the-art machine learning techniques. Our results open a venue for wider applications in quantum simulation, quantum chemistry and quantum supremacy tests using near-term quantum devices.