| Quantum approximate optimization of non-planar graph problems on a planar superconducting processor MP Harrigan, KJ Sung, M Neeley, KJ Satzinger, F Arute, K Arya, J Atalaya, ... Nature Physics 17 (3), 332-336, 2021 | 499 | 2021 |
| Tensorflow quantum: A software framework for quantum machine learning M Broughton, G Verdon, T McCourt, AJ Martinez, JH Yoo, SV Isakov, ... arXiv preprint arXiv:2003.02989, 2020 | 381 | 2020 |
| Layerwise learning for quantum neural networks A Skolik, JR McClean, M Mohseni, P van der Smagt, M Leib Quantum Machine Intelligence 3 (1), 1-11, 2021 | 259 | 2021 |
| Quantum agents in the gym: a variational quantum algorithm for deep q-learning A Skolik, S Jerbi, V Dunjko Quantum 6, 720, 2022 | 114 | 2022 |
| Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm M Streif, S Yarkoni, A Skolik, F Neukart, M Leib Physical Review A 104 (1), 012403, 2021 | 50 | 2021 |
| Hybrid quantum ResNet for car classification and its hyperparameter optimization A Sagingalieva, M Kordzanganeh, A Kurkin, A Melnikov, D Kuhmistrov, ... Quantum Machine Intelligence 5 (2), 38, 2023 | 34* | 2023 |
| Equivariant quantum circuits for learning on weighted graphs A Skolik, M Cattelan, S Yarkoni, T Bäck, V Dunjko npj Quantum Information 9 (1), 47, 2023 | 30 | 2023 |
| Robustness of quantum reinforcement learning under hardware errors A Skolik, S Mangini, T Bäck, C Macchiavello, V Dunjko EPJ Quantum Technology 10 (1), 1-43, 2023 | 12 | 2023 |
| Volkswagen and quantum computing: An industrial perspective S Yarkoni, M Leib, A Skolik, M Streif, F Neukart, D von Dollen Digitale Welt 3, 34-37, 2019 | 7 | 2019 |
| Quantum machine learning: on the design, trainability and noise-robustness of near-term algorithms A Skolik Leiden University, 2023 | | 2023 |
| Parameterized quantum circuits for reinforcement learning of classical rare dynamics S Khatri, A Wilms, L Ohff, A Skolik, J Eisert APS March Meeting Abstracts 2023, D73. 003, 2023 | | 2023 |
