Optimal quantum control for transmons with Reinforcement Learning

Noise in existing quantum processors significantly limits their performance, washing out their quantum advantage. Methods based on quantum control and error mitigation can alleviate the impact of noise using real-time calibration and post processing of quantum output. We describe a reinforcement learning algorithm to optimize gate control pulses with built in error-mitigation specifically targeting leakage errors in superconducting transmon qubits. While reinforcement learning is model-free, we investigate the benefits of providing some noise model information to the agent. We train the reinforcement learning agent directly on noisy hardware by targeting an X gate as a proof of concept, and describe the adaptation of our algorithm to other gates. We compare our algorithm to existing control strategies, including the widely used DRAG formula. Our algorithm is designed with feasibility as a priority, meaning it has a low overhead compared to other control pulse optimization techniques. Improvements to the speed and fidelity of gate operations open the possibility for more extensive applications in quantum simulation, quantum chemistry and other algorithms on near-term and future quantum devices.