Universal Quantum Control through Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework for quantum control that significantly improves the speed and fidelity of two-qubit gates, reducing errors and gate times compared to traditional methods.

Contribution

It presents a novel control optimization approach that incorporates control noise during training to enhance robustness and performance in quantum gate implementation.

Findings

Two-order-of-magnitude reduction in average gate error.

Up to tenfold reduction in gate time.

Enhanced robustness against stochastic control errors.

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.