Efficient quantum device tuning using machine learning by Lennon, DT

Identifying Pauli spin blockade using deep learning by Schuff, J, Lennon, DT, Geyer, S, Craig, DL, Fedele, F, Vigneau, F, Camenzind, LC, Kuhlmann, AV, Briggs, GAD, Zumbühl, DM, Sejdinovic, D, Ares, N

Quantum device fine-tuning using unsupervised embedding learning by van Esbroeck, NM, Lennon, DT, Moon, H, Nguyen, V, Vigneau, F, Camenzind, LC, Yu, L, Zumbühl, DM, Briggs, GAD, Sejdinovic, D, Ares, N

Deep reinforcement learning for efficient measurement of quantum devices by Nguyen, V, Orbell, SB, Lennon, DT, Moon, H, Vigneau, F, Camenzind, LC, Yu, L, Zumbuhl, DM, Briggs, GAD, Osborne, MA, Sejdinovic, D, Ares, N

Bridging the reality gap in quantum devices with physics-aware machine learning by Craig, DL, Moon, H, Fedele, F, Lennon, DT, van Straaten, B, Vigneau, F, Camenzind, LC, Zumbühl, DM, Briggs, GAD, Osborne, MA, Sejdinovic, D, Ares, N

Machine learning enables completely automatic tuning of a quantum device faster than human experts by Moon, H, Lennon, DT, Kirkpatrick, J, van Esbroeck, NMV, Camenzind, LC, Yu, L, Vigneau, F, Zumbühl, DM, Briggs, GAD, Osborne, MA, Sejdinovic, D, Laird, EA, Ares, N

Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning by Severin, B, Lennon, DT, Camenzind, LC, Vigneau, F, Fedele, F, Jirovec, D, Ballabio, A, Chrastina, D, Isella, G, de Kruijf, M, Carballido, MJ, Svab, S, Kuhlmann, AV, Geyer, S, Froning, FNM, Moon, H, Osborne, MA, Sejdinovic, D, Katsaros, G, Zumbühl, DM, Briggs, GAD, Ares, N