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To realize the full potential of quantum technologies, finding goodstrategies to control quantum information processing devices in real timebecomes increasingly important. Usually these strategies require a preciseunderstanding of the device itself, which is generally not available.Model-free reinforcement learning circumvents this need by discovering controlstrategies from scratch without relying on an accurate description of thequantum system. Furthermore, important tasks like state preparation, gateteleportation and error correction need feedback at time scales much shorterthan the coherence time, which for superconducting circuits is in themicrosecond range. Developing and training a deep reinforcement learning agentable to operate in this real-time feedback regime has been an open challenge.Here, we have implemented such an agent in the form of a latency-optimized deepneural network on a field-programmable gate array (FPGA). We demonstrate itsuse to efficiently initialize a superconducting qubit into a target state. Totrain the agent, we use model-free reinforcement learning that is based solelyon measurement data. We study the agent's performance for strong and weakmeasurements, and for three-level readout, and compare with simple strategiesbased on thresholding. This demonstration motivates further research towardsadoption of reinforcement learning for real-time feedback control of quantumdevices and more generally any physical system requiring learnable low-latencyfeedback control.

Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system