87 citations · 246 across the 8 of their papers we have counts for
8 papers
Virtual Gates Enabled by Digital Surrogate of Quantum Dot Devices
Alexander Lidiak, Jacob Swain, David L. Craig +12
Advances in quantum technologies are often limited by slow device characterization, complex tuning requirements, and scalability challenges. Spin qubits in electrostatically define…
Identifying Pauli spin blockade using deep learning
Jonas Schuff, Dominic T. Lennon, Simon Geyer +9
Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We presen…
Bridging the reality gap in quantum devices with physics-aware machine learning
D. L. Craig, H. Moon, F. Fedele +9
The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of mater…
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
B. Severin, D. T. Lennon, L. C. Camenzind +20
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a ke…
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…
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…