activity
20172026
most citedA hole spin qubit in a fin field-effect transistor above 4 kelvin

199 citations · 967 across the 28 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

cond-mat.mes-hall2020★ 6 cited

Isotropic and Anisotropic g-factor Corrections in GaAs Quantum Dots

Leon C. Camenzind, Simon Svab, Peter Stano +5

We experimentally determine isotropic and anisotropic g-factor corrections in lateral GaAs single-electron quantum dots. We extract the Zeeman splitting by measuring the tunnel rat…

cond-mat.mes-hall2020★ 4 cited

Quantum measurement induces a many-body transition

Michael S. Ferguson, Leon C. Camenzind, Clemens Müller +5

The current revolution in quantum technologies relies on the ability to isolate, coherently control, and measure the state of quantum systems. The act of measurement in quantum mec…

cond-mat.mes-hall2020★ 36 cited

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

V. Nguyen, S. B. Orbell, D. T. Lennon +9

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experie…

cond-mat.mes-hall2020★ 49 cited

Silicon quantum dot devices with a self-aligned second gate layer

Simon Geyer, Leon C. Camenzind, Lukas Czornomaz +5

We implement silicon quantum dot devices with two layers of gate electrodes using a self-alignment technique, which allows for ultra-small gate lengths and intrinsically perfect la…

cond-mat.mes-hall2020★ 164 cited

Ultrafast Hole Spin Qubit with Gate-Tunable Spin-Orbit Switch

F. N. M. Froning, L. C. Camenzind, O. A. H. van der Molen +4

A key challenge in quantum computation is the implementation of fast and local qubit control while simultaneously maintaining coherence. Qubits based on hole spins offer, through t…

cond-mat.mes-hall2020★ 31 cited

Quantum device fine-tuning using unsupervised embedding learning

N. M. van Esbroeck, D. T. Lennon, H. Moon +8

Quantum 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 the…

cond-mat.mes-hall2020★ 87 cited

Machine learning enables completely automatic tuning of a quantum device faster than human experts

H. Moon, D. T. Lennon, J. Kirkpatrick +10

Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explo…