Talk: Machine learning-based control of quantum devices and nanoscale thermodynamics

Organiser:
Max Planck Institute for the Science of Light
Marquardt Division
Speaker:
Natalia Ares, University of Oxford
Title:
Machine learning-based control of quantum devices and nanoscale thermodynamics
Abstract:
As devices are miniaturised to the nanoscale, fluctuations emerge as a defining factor, posing challenges for quantum device control. At the same time, they reveal the nuances of dissipation and thermalisation processes at this scale, opening opportunities to investigate thermodynamics in nanoscale systems and to apply these principles in new technologies.
In this seminar, I will show how advanced machine learning approaches enable efficient characterisation and control of quantum devices, including the first demonstration of fully automated spin-qubit tuning [1,2]. Alongside enabling automated operation, these methods can reveal otherwise inaccessible device properties [3], offering new physical insights into performance limits and variability.
I will then explore the complementary perspective: how fluctuations can be harnessed to drive device functionality. In particular, I will describe the realisation of a nanoscale clock driven by single-electron tunnelling [4]. Building on this concept, I will discuss how nonequilibrium phenomena may enable the development of nanoscale engines and refrigerators, and how these systems provide a platform to probe the fundamental thermodynamic costs of learning and information processing.

[1] J. Schuff, M. J. Carballido, M. Kotzagiannidis, J. C. Calvo, M. Caselli, J. Rawling, D. L. Craig, B. van Straaten, B. Severin, F. Fedele, S. Svab, P. Chevalier Kwon, R. S. Eggli, T. Patlatiuk, N. Korda, D. Zumbühl, N. Ares. Fully autonomous tuning of a spin qubit. arXiv:2402.03931.
[2] C. Carlsson, J. Saez-Mollejo, F. Fedele, S. Calcaterra, D. Chrastina, G. Isella, G. Katsaros, N. Ares. Automated all-RF tuning for spin qubit readout and control. arXiv:2506.10834.
[3] D.L. Craig, H. Moon, F. Fedele, D.T. Lennon, B. Van Straaten, F. Vigneau, L.C. Camenzind, D.M. Zumbühl, G.A.D. Briggs, M.A. Osborne, D. Sejdinovic, N. Ares, Bridging the reality gap in quantum devices with physics-aware machine learning, Phys. Rev. X 14, 011001 (2024)
[4] V. Wadhia, F. Meier, F. Fedele, R. Silva, N. Nurgalieva, D. L. Craig, D. Jirovec, J. Saez-Mollejo, A. Ballabio, D. Chrastina, G. Isella, M. Huber, M. T. Mitchison, P. Erker, N. Ares. Entropic costs of the quantum-to-classical transition in a microscopic clock. arXiv:2502.00096.