Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning

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.

arXiv comment15 pages, 10 figures (including supplemental material)

References in corpus (42)

• Machine learning and the physical sciences
• Strongly coupled magnons and cavity microwave photons
• Hybridizing ferromagnetic magnons and microwave photons in the quantum limit
• Towards quantum superpositions of a mirror
• Hybrid quantum systems based on magnonics
• Magnon-photon-phonon entanglement in cavity magnomechanics
• Bistability of Cavity Magnon Polaritons
• Using interference for high fidelity quantum state transfer in optomechanics
• Resolved-sideband cooling and measurement of a micromechanical oscillator close to the quantum limit
• Reinforcement Learning in Different Phases of Quantum Control
• Sideband Cooling Beyond the Quantum Limit with Squeezed Light
• A Scheme to Probe the Decoherence of a Macroscopic Object
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Quantum information processing with bosonic qubits in circuit QED
• Dissipative couplings in cavity magnonics
• Ultrasensitive optical magnetometry at the microscale
• When does reinforcement learning stand out in quantum control? A comparative study on state preparation
• Electron spin control of optically levitated nanodiamonds in vacuum
• Feedback cooling of a room temperature mechanical oscillator close to its motional groundstate
• Coherent Transport of Quantum States by Deep Reinforcement Learning
• Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
• Dissipative optomechanical preparation of macroscopic quantum superposition states
• Ground state cooling and high-fidelity quantum transduction via parametrically-driven bad-cavity optomechanics
• Optomechanical magnetometry with a macroscopic resonator
• Intracavity-squeezed optomechanical cooling
• Deep Reinforcement Learning Control of Quantum Cartpoles
• Quantum enhanced feedback cooling of a mechanical oscillator
• Searching for vector dark matter with an optomechanical accelerometer
• Quantum Spin Stabilized Magnetic Levitation
• Optomechanical cooling with intracavity squeezed light
• Cooling mechanical oscillators by coherent control
• A Reinforcement Learning approach for Quantum State Engineering
• Ramsey interferences and spin echoes from electron spins inside a levitating macroscopic particle
• Spin-mechanics with levitating ferromagnetic particles
• Faster State Preparation across Quantum Phase Transition Assisted by Reinforcement Learning
• Fast simulation of bosonic qubits via Gaussian functions in phase space
• Classifying global state preparation via deep reinforcement learning
• Generalizable control for multiparameter quantum metrology
• Real-time calibration of coherent-state receivers: learning by trial and error
• An opto-magneto-mechanical quantum interface between distant superconducting qubits
• Simulation of sympathetic cooling an optically levitated magnetic nanoparticle via coupling to a cold atomic gas
• Quantum optomechanics beyond the quantum coherent oscillation regime

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