Quantum 2025 – scientific programme
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THU: Thursday Contributed Sessions
THU 13: Poster Session: Applications
THU 13.87: Poster
Thursday, September 11, 2025, 16:30–18:30, ZHG Foyer 1. OG
Magneto-levitating Systems: Theory of Feedback Cooling and Intrinsic Dissipation — •Pietro Oreglia1, 2, Gianluigi Catelani3,4, Daniele Contessi1,5, Alessio Recati1, Andrea Vinante6, and Gianluca Rastelli1,2 — 1Pitaevskii BEC Center, CNR-INO and Dipartimento di Fisica, Università di Trento, I-38123 Trento, Italy — 2INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy — 3JARA Institute for Quantum Information (PGI-11), Forschungszentrum Jülich — 4Quantum Research Center, Technology Innovation Institute, Abu Dhabi — 5Traverlbrain srl, Trento, Italy — 66Istituto di Fotonica e Nanotecnologie-CNR and Fondazione Bruno Kessler, Trento, Italy
I study theoretically the fluctuation dynamics in the presence of feedback and the intrinsic mechanism of dissipation of a magneto-levitated system formed by a spherical micromagnet suspended in a superconducting trap. For the study of the feedback, I analyzed the cold damping method and applied it to describe a first experiment realized here in Trento, in which the microsphere, initially at the temperatures of a few kelvins, is cooled down up to tens of mK.
Moreover, I am also exploring the use of a machine learning approach, in which an agent learns the optimal way to induce the feedback in the loop, to improve the lowest achievable temperature.
In parallel, I investigate the dissipative effect due to the quasi-particle excitations hosted in the superconductor to set an upper bound to the intrinsic dissipation in this kind of system.
Keywords: magneto-levitating systems; machine learning; feedback cooling; intrinsic dissipation; quasi-particles in supercondutors