  To realize the full potential of quantum technologies, finding good
strategies to control quantum information processing devices in real time
becomes increasingly important. Usually these strategies require a precise
understanding of the device itself, which is generally not available.
Model-free reinforcement learning circumvents this need by discovering control
strategies from scratch without relying on an accurate description of the
quantum system. Furthermore, important tasks like state preparation, gate
teleportation and error correction need feedback at time scales much shorter
than the coherence time, which for superconducting circuits is in the
microsecond range. Developing and training a deep reinforcement learning agent
able 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 deep
neural network on a field-programmable gate array (FPGA). We demonstrate its
use to efficiently initialize a superconducting qubit into a target state. To
train the agent, we use model-free reinforcement learning that is based solely
on measurement data. We study the agent's performance for strong and weak
measurements, and for three-level readout, and compare with simple strategies
based on thresholding. This demonstration motivates further research towards
adoption of reinforcement learning for real-time feedback control of quantum
devices and more generally any physical system requiring learnable low-latency
feedback control.
