Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2204.07710v1
• Date: Sat, 16 Apr 2022 03:52:43 GMT
• Title: Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning
• Authors: Bijita Sarma, Sangkha Borah, A Kani, Jason Twamley
• Abstract summary: We propose a scheme to apply deep reinforcement learning (DRL) to achieve fast cooling of motional modes. We show how the scheme can be used effectively to accelerate the dynamic motional cooling of a macroscopic magnonic sphere. This offers a new and complete toolkit for rapid control and generation of macroscopic quantum states for application in quantum technologies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Achieving fast cooling of motional modes is a prerequisite for leveraging such bosonic quanta for high-speed quantum information processing. In this work, we address the aspect of reducing the time limit for cooling below that constrained by the conventional sideband cooling techniques; and propose a scheme to apply deep reinforcement learning (DRL) to achieve this. In particular, we have shown how the scheme can be used effectively to accelerate the dynamic motional cooling of a macroscopic magnonic sphere, and how it can be uniformly extended for more complex systems, for example, a tripartite opto-magno-mechanical system to obtain cooling of the motional mode below the time bound of coherent cooling. While conventional sideband cooling methods do not work beyond the well-known rotating wave approximation (RWA) regimes, our proposed DRL scheme can be applied uniformly to regimes operating within and beyond the RWA, and thus this offers a new and complete toolkit for rapid control and generation of macroscopic quantum states for application in quantum technologies.

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