Using Reinforcement Learning for Quantum Control in Magnetic Resonance

Robust control of a quantum system is fundamental to studying those systems or performing quantum simulation or computation. Reinforcement learning (RL) offers promising alternatives to existing methods for quantum control. We compare RL algorithms to gradient ascent pulse engineering (GRAPE) for both state-to-state transfer operations as well as the design of desired unitary operations on single- and two-qubit systems. GRAPE algorithms perform well when the system Hamiltonian is well-known, and when any uncertainties can be well parametrized a priori. On the other hand, RL algorithms, by treating the system’s dynamics as a black box and only receiving partial observations and reward signals from the system, have the potential to provide robust control of larger systems with more complex sources of error. The application of RL to Hamiltonian engineering of many-spin systems for quantum simulation and sensing is also considered.