| Training deep quantum neural networks K Beer, D Bondarenko, T Farrelly, TJ Osborne, R Salzmann, ... Nature communications 11 (1), 808, 2020 | 686 | 2020 |
| No free lunch for quantum machine learning K Poland, K Beer, TJ Osborne arXiv preprint arXiv:2003.14103, 2020 | 54 | 2020 |
| Quantum machine learning of graph-structured data K Beer, M Khosla, J Köhler, TJ Osborne, T Zhao Physical Review A 108 (1), 012410, 2023 | 26 | 2023 |
| Quantum neural networks K Beer arXiv preprint arXiv:2205.08154, 2022 | 16 | 2022 |
| Contextuality and bundle diagrams K Beer, TJ Osborne Physical Review A 98 (5), 052124, 2018 | 15 | 2018 |
| Training quantum neural networks on nisq devices K Beer, D List, G Müller, TJ Osborne, C Struckmann arXiv preprint arXiv:2104.06081, 2021 | 12 | 2021 |
| From categories to anyons: a travelogue K Beer, D Bondarenko, A Hahn, M Kalabakov, N Knust, L Niermann, ... arXiv preprint arXiv:1811.06670, 2018 | 10 | 2018 |
| Dissipative quantum generative adversarial networks K Beer, G Müller arXiv preprint arXiv:2112.06088, 2021 | 4 | 2021 |
| Phase-context decomposition of diagonal unitaries for higher-dimensional systems K Beer, FA Dziemba Physical Review A 93 (5), 052333, 2016 | 3 | 2016 |
| Gottfried Wilhelm Leibniz University Hanover K Beer Institute for Theoretical Physics, 2017 | | 2017 |
