Optimal Feedback Cooling of a Charged Levitated Nanoparticle with Adaptive Control

Area of Science:

• Quantum physics
• Nanotechnology
• Experimental physics

Background:

• Optically levitated nanoparticles are sensitive probes for fundamental physics.
• Achieving low temperatures is crucial for high-precision measurements.
• Existing cooling methods can be complex or slow.

Purpose of the Study:

• To develop a novel, efficient cooling protocol for optically levitated nanoparticles.
• To utilize machine learning for optimizing control parameters.
• To enhance force sensing capabilities and enable high-repetition-rate experiments.

Main Methods:

• Implemented an optimal control protocol based on a linear quadratic regulator.
• Employed a Coulomb force exerted by electrodes on a charged nanoparticle.
• Utilized a machine learning algorithm for rapid optimization of control gains (<5 seconds).

Main Results:

• Achieved a minimum center-of-mass temperature of 5 mK.
• Demonstrated transient speeds 10-600 times faster than conventional cold damping.
• Developed a simpler and more robust setup compared to optical feedback methods.

Conclusions:

• The optimal control protocol offers a significant advancement in cooling levitated nanoparticles.
• This technique is readily extendable to three-dimensional cooling.
• The method is highly suitable for high-repetition-rate studies and sensitive force detection.