Speaker
Description
Finding strategies to control quantum information processing devices in real-time becomes increasingly demanding as they grow in size and complexity. Reinforcement learning promises to overcome this challenge by uncovering the underlying system dynamics without relying on a specific model. Here, we implement a deep neural network on a field-programmable gate array (FPGA) and demonstrate its use as a real-time reinforcement learning agent to efficiently initialize a superconducting qubit into its ground state. The agent repeatedly measures the state of the qubit and chooses on a sub-microsecond time scale whether to idle, to apply a bit-flip gate, or to terminate. After the agent chooses to terminate the initialization process, we perform a validation measurement to infer the probability of having successfully initialized the ground state. To train the agent, we use model-free reinforcement learning that is based solely on measurement data.
