Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach to accelerate motional cooling in quantum systems, surpassing traditional methods and applicable across different operational regimes, enabling faster quantum state preparation.

Contribution

The paper presents a novel DRL-based scheme for rapid motional cooling that works beyond the rotating wave approximation, extending cooling capabilities to complex quantum systems.

Findings

DRL accelerates motional cooling in magnonic spheres.

The scheme surpasses conventional sideband cooling limits.

Applicable to regimes beyond the RWA.

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.