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- W2792427217 abstract "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." @default.
- W2792427217 created "2018-03-29" @default.
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- W2792427217 date "2018-03-05" @default.
- W2792427217 modified "2023-10-16" @default.
- W2792427217 title "Universal Quantum Control through Deep Reinforcement Learning" @default.
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- W2792427217 hasPublicationYear "2018" @default.
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