| A rigorous and robust quantum speed-up in supervised machine learning Y Liu, S Arunachalam, K Temme Nature Physics 17 (9), 1013-1017, 2021 | 639 | 2021 |
| Guest column: A survey of quantum learning theory S Arunachalam, R De Wolf ACM Sigact News 48 (2), 41-67, 2017 | 240 | 2017 |
| Optimizing quantum optimization algorithms via faster quantum gradient computation A Gilyén, S Arunachalam, N Wiebe Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 194 | 2019 |
| On the robustness of bucket brigade quantum RAM S Arunachalam, V Gheorghiu, T Jochym-O’Connor, M Mosca, ... New Journal of Physics 17 (12), 123010, 2015 | 188 | 2015 |
| Sample-efficient learning of interacting quantum systems A Anshu, S Arunachalam, T Kuwahara, M Soleimanifar Nature Physics 17 (8), 931-935, 2021 | 165* | 2021 |
| Optimal quantum sample complexity of learning algorithms S Arunachalam, R De Wolf Journal of Machine Learning Research 19 (71), 1-36, 2018 | 133 | 2018 |
| Quantum computing for high-energy physics: State of the art and challenges A Di Meglio, K Jansen, I Tavernelli, C Alexandrou, S Arunachalam, ... PRX Quantum 5 (3), 037001, 2024 | 124* | 2024 |
| A survey on the complexity of learning quantum states A Anshu, S Arunachalam Nature Reviews Physics 6 (1), 59-69, 2024 | 80 | 2024 |
| Quantum statistical query learning S Arunachalam, AB Grilo, H Yuen arXiv preprint arXiv:2002.08240, 2020 | 41 | 2020 |
| Quantum query algorithms are completely bounded forms S Arunachalam, J Briët, C Palazuelos SIAM Journal on Computing 48 (3), 903-925, 2019 | 40 | 2019 |
| Is absolute separability determined by the partial transpose? S Arunachalam, N Johnston, V Russo Quantum Information & Computation 15 (7-8), 694-720, 2014 | 37 | 2014 |
| Quantum boosting S Arunachalam, R Maity International Conference on Machine Learning, 377-387, 2020 | 36 | 2020 |
| Simpler (classical) and faster (quantum) algorithms for Gibbs partition functions S Arunachalam, V Havlicek, G Nannicini, K Temme, P Wocjan Quantum 6, 789, 2022 | 34 | 2022 |
| Two new results about quantum exact learning S Arunachalam, S Chakraborty, T Lee, M Paraashar, R de Wolf Quantum 5, 587, 2021 | 33 | 2021 |
| Optimal algorithms for learning quantum phase states S Arunachalam, S Bravyi, A Dutt, TJ Yoder 18th Conference on the Theory of Quantum Computation, Communication and …, 2022 | 28 | 2022 |
| Quantum hardness of learning shallow classical circuits S Arunachalam, AB Grilo, A Sundaram SIAM Journal on Computing 50 (3), 972-1013, 2021 | 27 | 2021 |
| Improved bounds on Fourier entropy and min-entropy S Arunachalam, S Chakraborty, M Koucký, N Saurabh, R De Wolf ACM Transactions on Computation Theory (TOCT) 13 (4), 1-40, 2021 | 23 | 2021 |
| Private learning implies quantum stability S Arunachalam, Y Quek, J Smolin Advances in Neural Information Processing Systems 34, 2021 | 22* | 2021 |
| Optimizing the number of gates in quantum search S Arunachalam, R De Wolf Quantum Information and Computation, 2017, 2015 | 22 | 2015 |
| Quantum coupon collector S Arunachalam, A Belovs, AM Childs, R Kothari, A Rosmanis, R De Wolf 15th Conference on the Theory of Quantum Computation, Communication and …, 2020 | 20 | 2020 |
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