Universal quantum control through deep reinforcement learning

By M. Niu, Sergio Boixo, Vadim N. Smelyanskiy, Hartmut Neven. npj Quantum Information, 2018

394 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3ada65624811cfc46bc113509f3120100bcf4f52

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Citations in the corpus

Reinforcement Learning in Different Phases of Quantum Control (2017, background): “[57] Murphy Yuezhen Niu, Sergio Boixo, Vadim Smelyan-”
A high-bias, low-variance introduction to Machine Learning for physicists (2018, neutral): “…2018; Bukov et al., 2018; Cárdenas-López et al., 2017; Chen et al., 2014; Chen and Xue, 2019; Dunjko et al., 2017; Fösel et al., 2018; Lamata, 2017; Melnikov et al., 2017; Neukart et al., 2017; Niu et al., 2018; Ramezanpour, 2017; Reddy et al., 2016b; Sriarunothai et al., 2017; Zhang et al., 2018).”
Photonic quantum information processing: A concise review (2019, methodology): “ML provides a method to work with quantum protocols operating in an environment of unknown or uncharacterized noise, or where the full ab initio modeling of the protocol is intractable, 298 and can be applied to PQC and other systems.”
Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe (2022, background): “Basic questions such as the implementation of quantum gates [30], the transport of quantum states [470] and robust control against leakage or control errors [439, 626] have been answered.”