| What can we learn from quantum convolutional neural networks? C Umeano, AE Paine, VE Elfving, O Kyriienko arXiv preprint arXiv:2308.16664, 2023 | 5 | 2023 |
| What can we learn from quantum convolutional neural networks?(2023) C Umeano, AE Paine, VE Elfving, O Kyriienko arXiv preprint arXiv:2308.16664, 0 | 5 | |
| Quantum topological data analysis via the estimation of the density of states S Scali, C Umeano, O Kyriienko arXiv preprint arXiv:2312.07115, 2023 | 1 | 2023 |
| Quantum Algorithms: A Review C Umeano Imperial College London, 2021 | 1 | 2021 |
| Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification? C Umeano, S Scali, O Kyriienko arXiv preprint arXiv:2409.01496, 2024 | | 2024 |
| Quantum subspace expansion approach for simulating dynamical response functions of Kitaev spin liquids C Umeano, F Jamet, LP Lindoy, I Rungger, O Kyriienko arXiv preprint arXiv:2407.04205, 2024 | | 2024 |
| Ground state-based quantum feature maps C Umeano, O Kyriienko arXiv preprint arXiv:2404.07174, 2024 | | 2024 |
| Quantum topological data analysis: using Fourier analysis to learn topological properties S Scali, O Kyriienko, C Umeano Bulletin of the American Physical Society, 2024 | | 2024 |
| The topology of data hides in quantum thermal states S Scali, C Umeano, O Kyriienko arXiv preprint arXiv:2402.15633, 2024 | | 2024 |
| Geometric quantum machine learning of BQP protocols and latent graph classifiers C Umeano, VE Elfving, O Kyriienko arXiv preprint arXiv:2402.03871, 2024 | | 2024 |
Oleksandr KyriienkoProfessor at the University of Exeter在 exeter.ac.uk 的电子邮件经过验证
