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Researchers utilized a Long Short-Term Memory (LSTM) network to model complex non-Markovian dynamics in a hybrid magnon-atom system, enabling efficient feedback control that was previously computationally prohibitive. The study reveals that entanglement strength oscillates predictably with mirror distance, offering a new method for quantum state engineering in hybrid platforms without solving the full Schrödinger equation. This approach advances practical control mechanisms for quantum networks by leveraging machine learning to stabilize intracavity states under time-delayed feedback.
Magnon-atom entanglement oscillates with mirror distance
Researchers demonstrated feedback control of a magnon-atom system, finding that magnon-atom entanglement’s strength varies periodically with mirror distance.
Researchers have, for the first time, incorporated a magnon into a quantum system with feedback control, a step enabled by employing a Long Short-Term Memory network to efficiently model the complex dynamics. The work reveals that the level of magnon-atom entanglement, measured by a property called concurrence, oscillates predictably as the distance between the cavity mirrors changes. These results demonstrate a new approach to quantum state engineering in hybrid magnonic systems, potentially advancing quantum networks and information processing, and show that feedback significantly enhances both antibunching and bunching when the detuning is varied. A Long Short-Term Memory network, a form of machine learning, was employed…
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