| Machine learning enables completely automatic tuning of a quantum device faster than human experts H Moon, DT Lennon, J Kirkpatrick, NM van Esbroeck, LC Camenzind, ... Nature communications 11 (1), 4161, 2020 | 83 | 2020 |
| Efficiently measuring a quantum device using machine learning DT Lennon, H Moon, LC Camenzind, L Yu, DM Zumbühl, GAD Briggs, ... npj Quantum Information 5 (1), 79, 2019 | 73 | 2019 |
| Quantum device fine-tuning using unsupervised embedding learning NM van Esbroeck, DT Lennon, H Moon, V Nguyen, F Vigneau, ... New Journal of Physics 22 (9), 095003, 2020 | 40 | 2020 |
| Deep reinforcement learning for efficient measurement of quantum devices V Nguyen, SB Orbell, DT Lennon, H Moon, F Vigneau, LC Camenzind, ... npj Quantum Information 7 (1), 100, 2021 | 28* | 2021 |
| Bridging the reality gap in quantum devices with physics-aware machine learning DL Craig, H Moon, F Fedele, DT Lennon, B van Straaten, F Vigneau, ... Physical Review X 14 (1), 011001, 2024 | 15 | 2024 |
| Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning B Severin, DT Lennon, LC Camenzind, F Vigneau, F Fedele, D Jirovec, ... arXiv preprint arXiv:2107.12975, 2021 | 12 | 2021 |
| Identifying Pauli spin blockade using deep learning J Schuff, DT Lennon, S Geyer, DL Craig, F Fedele, F Vigneau, ... Quantum 7, 1077, 2023 | 7 | 2023 |
| Efficiently measuring a quantum device using machine learning, npj Quantum Inf DT Lennon, H Moon, LC Camenzind, L Yu, DM Zumbühl, GAD Briggs, ... | 6 | 2019 |
| Efficient quantum device tuning using machine learning DT Lennon University of Oxford, 2021 | 2 | 2021 |
| Superconducting quantum processor tune-up automation D Lennon Bulletin of the American Physical Society, 2024 | | 2024 |
| Identifying Pauli spin blockade using deep learning N Ares, J Schuff, D Lennon, S Geyer, D Craig, F Fedele, F Vigneau, ... | | 2022 |
| Identifying Pauli spin blockade using deep learning with scarce experimental data J Schuff, D Lennon, S Geyer, D Craig, L Camenzind, F Fedele, F Vigneau, ... APS March Meeting Abstracts 2022, Q43. 010, 2022 | | 2022 |
| Benchmarking single-electron transistor charge sensitivity using machine learning J Chittock-Wood, D Lennon, B Govoreanu, S Kubicek, S Patomaki, ... APS March Meeting Abstracts 2022, W39. 008, 2022 | | 2022 |
| Machine Learning for tuning, controlling, and optimizing semiconductor spin qubits L Camenzind, D Lennon, V Nguyen, B Severin, N van Esbroeck, ... APS March Meeting Abstracts 2022, Q43. 001, 2022 | | 2022 |
| Deep Learning-Based Prediction and Optimal Sequential Measurement of a Quantum Dot D Lennon, H Moon, M Osborne, L Camenzind, L Yu, D Zumbuhl, G Briggs, ... APS March Meeting Abstracts 2019, G70. 382, 2019 | | 2019 |
| Controlling Quantum Device Measurement using Deep Rein-forcement Learning V Nguyen, DT Lennon, H Moon, NM van Esbroeck, D Sejdinovic, ... | | |
