Jonas Schuff
University of Oxford
9 Papers
33 Citations
Jonas Schuff is an academic researcher from University of Oxford. The author has contributed to research in topics: Reinforcement learning & Quantum sensor. The author has an hindex of 3, co-authored 4 publications. Previous affiliations of Jonas Schuff include University of Tübingen.
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Papers
Improving the dynamics of quantum sensors with reinforcement learning
TL;DR: In this paper, the cross-entropy method of reinforcement learning was used to optimize the strength and position of control pulses in quantum-chaotic sensors with super-radiant damping.
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Improving the dynamics of quantum sensors with reinforcement learning
TL;DR: In this paper, the cross-entropy method of reinforcement learning was used to optimize the strength and position of control pulses in quantum-chaotic sensors with super-radiant damping.
26
Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
TL;DR: In this paper, neural networks are trained to become fast and strong experiment-design heuristics using a combination of an evolutionary strategy and reinforcement learning for frequency estimation of a qubit that suffers from dephasing.
7
Journal Article
Identifying Pauli spin blockade using deep learning
Jonas Schuff,D.T. Lennon,Simon Geyer,D. L. Craig,Federico Fedele,F. Vigneau,Leon C. Camenzind,Andreas V. Kuhlmann,G. Andrew D. Briggs,D. Zumbuhl,Dino Sejdinovic,N. Ares +11 more
TL;DR: In this article , a machine learning algorithm was proposed to identify the Pauli spin blockade (PSB) using charge transport measurements, which achieved an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability.
2
Fully autonomous tuning of a spin qubit
Jonas Schuff,M.J. Saavedra Carballido,Madeleine Kotzagiannidis,Juan Carlos Calvo,Marco Caselli,Jacob Henry Rawling,David L. Craig,Barnaby van Straaten,Brandon Severin,Federico Fedele,Simon Svab,Pierre Chevalier Kwon,R. S. Eggli,Taras Patlatiuk,Nathan Korda,Dominik M. Zumbühl,Natalia Ares +16 more
TL;DR: Fully autonomous tuning of a spin qubit using deep learning and Bayesian optimization enables complete control over qubit operation without human intervention.
2