Funding
EMP is funded through the Horizon2020 programme of the European Commission. 
Selected Publications
- Photon Transport in a Bose-Hubbard Chain of Superconducting Artificial Atoms
G. P. Fedorov et al., Phys. Rev. Lett. 126, 180503 (2021) - Path-Dependent Supercooling of the
He3 Superfluid A-B Transition
Dmytro Lotnyk et al., Phys. Rev. Lett. 126, 215301 (2021) - Superconductivity in an extreme strange metal
D. H. Nguyen et al., Nat Commun 12, 4341 (2021) - High-Q Silicon Nitride Drum Resonators Strongly Coupled to Gates
Xin Zhou et al., Nano Lett. 21, 5738-5744 (2021) - Measurement of the 229Th isomer energy with a magnetic micro-calorimeter
T. Sikorsky et al., Phys. Rev. Lett. 125 (2020) 142503
Quantum device fine-tuning using unsupervised embedding learning
N.M. van Esbroeck, D.T. Lennon, H. Moon, V. Nguyen, F. Vigneau, L.C. Camenzind, L. Yu, D.M. Zumbühl, G.A.D. Briggs, D. Sejdinovic, N. AresQuantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.
New J. Phys. 22,095003 (2020)
doi: 10.1088/1367-2630/abb64c
arxiv: https://arxiv.org/abs/2001.04409