Realizing a deep reinforcement learning agent for real-time quantum feedback.

ABSTRACT: Realizing the full potential of quantum technologies requires precise real-time control on time scales much shorter than the coherence time. Model-free reinforcement learning promises to discover efficient feedback strategies from scratch without relying on a description of the quantum system. However, developing and training a reinforcement learning agent able to operate in real-time using feedback has been an open challenge. Here, we have implemented such an agent for a single qubit as a sub-microsecond-latency neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit and train the agent based solely on measurements. Our work is a first step towards adoption of reinforcement learning for the control of quantum devic

SUBMITTER: Reuer K 

PROVIDER: S-EPMC10628214 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature