Quantum Coherence Lab

Zumbühl Group

 

News from our Group

Dr. Mohammad Samani joins our group as a postdoc

Having received his PhD from the University of British Columbia, Canada, in Prof. Joshua Folk's group on "Local probe of electronic states in high mobility quantum Hall samples", Mohammad is starting on his postdoc on the microkelvin project working on opening the sub-mK temperature range for nanoelectronics / quantum transport experiments. Welcome to the group, looking forward to work with you!

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PhD defense of Bilal Kalyoncu

Yemliha "Bilal" Kalyoncu successfully defended his PhD thesis "Hydrogen Plasma Etched Graphene Nanoribbons" today with Dr. Jonathan Prance, Lancaster University, as coreferee and Prof. Ilaria Zardo chairing. Congratulations, Bilal, on a very nice thesis and exam, and thanks very much for all the work!

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Miguel Carbadillo joins our group as a PhD student

Having received his MSc in Physics from the University of Basel on the thermal conductivity of nanowires using Raman
thermometry in Prof. Ilaria Zardo's group, Miguel is starting his PhD thesis on the Ge/Si nanowire project, developing these as a platform for spin qubits and exotic quantum matter such as Majorana fermions. Welcome to the group, looking forward to work with you!

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New on arXiv: Closed-form weak localization with arbitrary Rashba and Dresselhaus interactions

We derive a closed-form expression for the weak localization corrections to the magnetoconductivity of a 2D electron system with arbitrary Rashba and both linear and cubic Dresselhaus spin-orbit interactions in a perpendicular magnetic field geometry. In a reference frame with an in-plane z-axis along the spin-helix symmetry direction, we find a general decoupling algorithm for the Cooperon spin modes that leads to a representation invariant, closed-form expression. The anisotropy of the eff ective spin relaxation rates is fundamental to understanding spin-orbit coupling in quantum transport. Marinescu et al. arXiv:1811.04488

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New ArXiv submission: Effciently measuring a quantum device using machine learning

Tuning quantum devices is becoming time-consuming as systems are scaled up, e.g. to numerous gates or contacts, and will soon become intractable without automation. Here, we present measurements on a quantum dot done by a machine learning algorithm. This selects the most informative measurements to perform next using information theory and a probabilistic deep-generative model capable of generating multiple full-resolution reconstructions from scattered partial measurements. We demonstrate that the algorithm outperforms standard grid scan techniques, reducing the measurement time by a factor of ~4,  thus laying the foundation for automated control of large quantum circuits. Lennon et al., arxiv.org/abs/1810.10042

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