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

• URL: http://arxiv.org/abs/2210.16715v1
• Date: Sun, 30 Oct 2022 01:31:20 GMT
• Title: Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
• Authors: Kevin Reuer, Jonas Landgraf, Thomas F\"osel, James O'Sullivan, Liberto Beltr\'an, Abdulkadir Akin, Graham J. Norris, Ants Remm, Michael Kerschbaum, Jean-Claude Besse, Florian Marquardt, Andreas Wallraff, Christopher Eichler
• Abstract summary: We develop 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. We study the agent's performance for strong and weak measurements, and for three-level readout, and compare with simple strategies based on thresholding.
• Score: 3.598535368045164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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