[Paper Review] Optimal quantum control of mechanical motion at room temperature: ground-state cooling

Lorenzo Magrini, Philipp Rosenzweig+7 more|arXiv (Cornell University)|Dec 30, 2020

Quantum Information and Cryptography15 citations

TL;DR

This paper demonstrates real-time optimal quantum control of a room-temperature optically trapped nanoparticle, using quantum-limited position sensing and Kalman filtering for state estimation. The method achieves ground-state cooling with a mean occupation number of $ n = 0.56 \pm 0.02 $, stabilizing the mechanical oscillator near the quantum ground state despite thermal noise.

ABSTRACT

The ability to accurately control the dynamics of physical systems by measurement and feedback is a pillar of modern engineering. Today, the increasing demand for applied quantum technologies requires to adapt this level of control to individual quantum systems. Achieving this in an optimal way is a challenging task that relies on both quantum-limited measurements and specifically tailored algorithms for state estimation and feedback. Successful implementations thus far include experiments on the level of optical and atomic systems. Here we demonstrate real-time optimal control of the quantum trajectory of an optically trapped nanoparticle. We combine confocal position sensing close to the Heisenberg limit with optimal state estimation via Kalman filtering to track the particle motion in phase space in real time with a position uncertainty of 1.3 times the zero point fluctuation. Optimal feedback allows us to stabilize the quantum harmonic oscillator to a mean occupation of $n=0.56\pm0.02$ quanta, realizing quantum ground state cooling from room temperature. Our work establishes quantum Kalman filtering as a method to achieve quantum control of mechanical motion, with potential implications for sensing on all scales.

Motivation & Objective

• To extend real-time optimal quantum control to mechanical systems at room temperature, where thermal noise dominates.
• To achieve ground-state cooling of a mechanical oscillator using measurement-based feedback.
• To implement quantum-limited position sensing and optimal state estimation via Kalman filtering in a macroscopic mechanical system.
• To demonstrate that quantum control techniques, previously used in atomic and optical systems, can be adapted to mesoscopic mechanical motion.

Proposed method

• Employed confocal position sensing to achieve a position uncertainty of 1.3 times the zero-point fluctuation, approaching the Heisenberg limit.
• Applied Kalman filtering for real-time optimal state estimation of the nanoparticle's phase-space trajectory.
• Implemented a feedback control loop based on the estimated state to stabilize the mechanical oscillator.
• Used a feedback algorithm tailored to minimize the mean occupation number of the harmonic oscillator.
• Combined high-fidelity measurement with optimal estimation to suppress thermal fluctuations and cool the system.

Experimental results

Research questions

• RQ1 Can real-time optimal quantum control be achieved in a room-temperature mechanical system using quantum-limited measurements?
• RQ2 To what extent can Kalman filtering enable accurate state estimation for feedback control in a macroscopic mechanical oscillator?
• RQ3 What is the lowest achievable mean occupation number in a mechanical system under feedback control at room temperature?
• RQ4 Can quantum control techniques developed for atomic and optical systems be successfully transferred to mechanical motion?

Key findings

• The system achieved a mean occupation number of $ n = 0.56 \pm 0.02 $ quanta, demonstrating ground-state cooling from room temperature.
• Position uncertainty was measured at 1.3 times the zero-point fluctuation, indicating near-Heisenberg-limited sensing.
• Kalman filtering enabled real-time, optimal state estimation of the nanoparticle's motion in phase space.
• Feedback control successfully suppressed thermal motion, stabilizing the oscillator near the quantum ground state.