| Qiskit: An open-source framework for quantum computing G Aleksandrowicz, T Alexander, P Barkoutsos, L Bello, Y Ben-Haim, ... Accessed on: Mar 16, 61, 2019 | 894 | 2019 |
| Discovering physical concepts with neural networks R Iten, T Metger, H Wilming, L del Rio, R Renner Phys. Rev. Lett. 124, 010508 (2020), 2018 | 510 | 2018 |
| Option pricing using quantum computers N Stamatopoulos, DJ Egger, Y Sun, C Zoufal, R Iten, N Shen, S Woerner Quantum 4, 291, 2020 | 286 | 2020 |
| Quantum circuits for isometries R Iten, R Colbeck, I Kukuljan, J Home, M Christandl Physical Review A 93 (3), 032318, 2016 | 184 | 2016 |
| Introduction to universalqcompiler R Iten, O Reardon-Smith, E Malvetti, L Mondada, G Pauvert, E Redmond, ... arXiv preprint arXiv:1904.01072, 2019 | 49 | 2019 |
| Quantum circuits for sparse isometries E Malvetti, R Iten, R Colbeck Quantum 5, 412, 2021 | 44 | 2021 |
| Exact and practical pattern matching for quantum circuit optimization R Iten, R Moyard, T Metger, D Sutter, S Woerner ACM Transactions on Quantum Computing 3 (1), 1-41, 2022 | 41 | 2022 |
| Computing quantum channel capacities N Ramakrishnan, R Iten, VB Scholz, M Berta IEEE Transactions on Information Theory 67 (2), 946-960, 2020 | 33* | 2020 |
| Quantum circuits for quantum channels R Iten, R Colbeck, M Christandl Physical Review A 95 (5), 052316, 2017 | 30 | 2017 |
| Operationally meaningful representations of physical systems in neural networks HP Nautrup, T Metger, R Iten, S Jerbi, LM Trenkwalder, H Wilming, ... Machine Learning: Science and Technology 3 (4), 045025, 2022 | 27 | 2022 |
| Pretty good measures in quantum information theory R Iten, JM Renes, D Sutter IEEE Transactions on Information Theory 63 (2), 1270-1279, 2016 | 23 | 2016 |
| Smooth manifold structure for extreme channels R Iten, R Colbeck Journal of Mathematical Physics 59 (1), 2018 | 5 | 2018 |
| Relations between different quantum R\'enyi divergences R Iten arXiv preprint arXiv:2012.08327, 2020 | 2 | 2020 |
| Artificial Intelligence for Scientific Discoveries: Extracting Physical Concepts from Experimental Data Using Deep Learning R Iten | 1* | 2023 |
| Representation learning for discovering physical concepts R Iten ETH Zurich, 2020 | | 2020 |
| Artificial Intelligence for Scientific Discoveries R Iten | | |
Roger ColbeckDepartment of Mathematics, University of York在 york.ac.uk 的电子邮件经过验证
