Reinforcement learning for quantum memory

The past few years have seen dramatic demonstrations of the power of neural networks to challenging real-world applications in many domains. In the search for optimal control sequences, where the success can only be judged with some time-delay, reinforcement learning is the method of choice. We have explored how a neural-network based agent can be trained to generate optimal control sequences for quantum feedback, where the agent interacts with a quantum system, using reinforcement learning. We apply this to the problem of stabilizing quantum memories based on few-qubit systems, where the qubit layout and available set of gates is specified by the user.