| Training deep quantum neural networks K Beer, D Bondarenko, T Farrelly, TJ Osborne, R Salzmann, ... Nature communications 11 (1), 808, 2020 | 561 | 2020 |
| Efficient learning for deep quantum neural networks B Kerstin, B Dmytro, F Terry, O Tobias, S Robert, W Ramona Nature, 2019 | 561* | 2019 |
| Quantum autoencoders to denoise quantum data D Bondarenko, P Feldmann Physical review letters 124 (13), 130502, 2020 | 107 | 2020 |
| Momentum entanglement for atom interferometry F Anders, A Idel, P Feldmann, D Bondarenko, S Loriani, K Lange, J Peise, ... Physical Review Letters 127 (14), 140402, 2021 | 52 | 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 | 9 | 2018 |
| Learning Quantum Processes with Memory--Quantum Recurrent Neural Networks D Bondarenko, R Salzmann, VS Schmiesing arXiv preprint arXiv:2301.08167, 2023 | 2 | 2023 |
| Constructing k-local parent Lindbladians for matrix product density operators D Bondarenko arXiv preprint arXiv:2110.13134, 2021 | 2 | 2021 |
| Constructing networks of quantum channels for state preparation D Bondarenko Hannover: Institutionelles Repositorium der Leibniz Universität Hannover, 2021 | 1 | 2021 |
| Tree tensor network approximations to conformal field theories D Bondarenko https://www.theorie.physik.uni-muenchen.de/TMP/theses/bondarenkothesis.pdf, 2017 | | 2017 |
