Abstract
AbstractRealizing 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 devices and more generally any physical device requiring low-latency feedback.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference55 articles.
1. Wiseman, H. & Milburn, G. Quantum Measurement and Control (Cambridge University Press, 2009).
2. Zhang, J., Liu, Y.-X., Wu, R.-B., Jacobs, K. & Nori, F. Quantum feedback: Theory, experiments, and applications. Phys. Rep. 679, 1 (2017).
3. Ristè, D., Bultink, C. C., Lehnert, K. W. & DiCarlo, L. Feedback control of a solid-state qubit using high-fidelity projective measurement. Phys. Rev. Lett. 109, 240502 (2012).
4. Campagne-Ibarcq, P. et al. Persistent control of a superconducting qubit by stroboscopic measurement feedback. Phys. Rev. X 3, 021008 (2013).
5. Salathé, Y. et al. Low-latency digital signal processing for feedback and feedforward in quantum computing and communication. Phys. Rev. Appl. 9, 034011 (2018).
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