Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements

Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in...

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Bibliographic Details
Published inQuantum (Vienna, Austria) Vol. 6; p. 747
Main Authors Porotti, Riccardo, Essig, Antoine, Huard, Benjamin, Marquardt, Florian
Format Journal Article
LanguageEnglish
Published Verein 2022
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
Subjects
Physics
Quantum Physics
photon
feedback
cavity
nonlinear
stability
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Summary:Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in this regard. It may provide new answers to difficult questions, such as whether nonlinear measurements can compensate for linear, constrained control. Here we show that reinforcement learning can successfully discover such feedback strategies, without prior knowledge. We illustrate this for state preparation in a cavity subject to quantum-non-demolition detection of photon number, with a simple linear drive as control. Fock states can be produced and stabilized at very high fidelity. It is even possible to reach superposition states, provided the measurement rates for different Fock states can be controlled as well.
ISSN:2521-327X
2521-327X
DOI:10.22331/q-2022-06-28-747