| A threshold for quantum advantage in derivative pricing S Chakrabarti, R Krishnakumar, G Mazzola, N Stamatopoulos, S Woerner, ... Quantum 5, 463, 2021 | 149 | 2021 |
| Quantum Wasserstein generative adversarial networks S Chakrabarti, H Yiming, T Li, S Feizi, X Wu Advances in Neural Information Processing Systems 32, 2019 | 118 | 2019 |
| Sublinear quantum algorithms for training linear and kernel-based classifiers T Li, S Chakrabarti, X Wu International Conference on Machine Learning, 3815-3824, 2019 | 84 | 2019 |
| Quantum algorithms and lower bounds for convex optimization S Chakrabarti, AM Childs, T Li, X Wu Quantum 4, 221, 2020 | 79 | 2020 |
| Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem R Shaydulin, C Li, S Chakrabarti, M DeCross, D Herman, N Kumar, ... Science Advances 10 (22), eadm6761, 2024 | 62 | 2024 |
| Quantum machine learning for finance ICCAD special session paper M Pistoia, SF Ahmad, A Ajagekar, A Buts, S Chakrabarti, D Herman, S Hu, ... 2021 IEEE/ACM international conference on computer aided design (ICCAD), 1-9, 2021 | 60 | 2021 |
| The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ans\" atze E Fontana, D Herman, S Chakrabarti, N Kumar, R Yalovetzky, J Heredge, ... arXiv preprint arXiv:2309.07902, 2023 | 57 | 2023 |
| A convergence theory for over-parameterized variational quantum eigensolvers X You, S Chakrabarti, X Wu arXiv preprint arXiv:2205.12481, 2022 | 44 | 2022 |
| Constrained optimization via quantum zeno dynamics D Herman, R Shaydulin, Y Sun, S Chakrabarti, S Hu, P Minssen, A Rattew, ... Communications Physics 6 (1), 219, 2023 | 35 | 2023 |
| Parameter setting in quantum approximate optimization of weighted problems SH Sureshbabu, D Herman, R Shaydulin, J Basso, S Chakrabarti, Y Sun, ... Quantum 8, 1231, 2024 | 31 | 2024 |
| Quantum algorithm for estimating volumes of convex bodies S Chakrabarti, AM Childs, SH Hung, T Li, C Wang, X Wu ACM Transactions on Quantum Computing 4 (3), 1-60, 2023 | 29 | 2023 |
| Quantum deep hedging S Raj, I Kerenidis, A Shekhar, B Wood, J Dee, S Chakrabarti, R Chen, ... Quantum 7, 1191, 2023 | 27 | 2023 |
| Hardness of the maximum-independent-set problem on unit-disk graphs and prospects for quantum speedups RS Andrist, MJA Schuetz, P Minssen, R Yalovetzky, S Chakrabarti, ... Physical Review Research 5 (4), 043277, 2023 | 23 | 2023 |
| Characterizing barren plateaus in quantum ansätze with the adjoint representation E Fontana, D Herman, S Chakrabarti, N Kumar, R Yalovetzky, J Heredge, ... Nature Communications 15 (1), 7171, 2024 | 19 | 2024 |
| Numerical evidence against advantage with quantum fidelity kernels on classical data L Slattery, R Shaydulin, S Chakrabarti, M Pistoia, S Khairy, SM Wild Physical Review A 107 (6), 062417, 2023 | 18 | 2023 |
| Sublinear classical and quantum algorithms for general matrix games T Li, C Wang, S Chakrabarti, X Wu Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8465-8473, 2021 | 17 | 2021 |
| Alignment between initial state and mixer improves QAOA performance for constrained optimization Z He, R Shaydulin, S Chakrabarti, D Herman, C Li, Y Sun, M Pistoia npj Quantum Information 9 (1), 121, 2023 | 15 | 2023 |
| Analyzing convergence in quantum neural networks: Deviations from neural tangent kernels X You, S Chakrabarti, B Chen, X Wu International Conference on Machine Learning, 40199-40224, 2023 | 15 | 2023 |
| Universal quantum speedup for branch-and-bound, branch-and-cut, and tree-search algorithms S Chakrabarti, P Minssen, R Yalovetzky, M Pistoia arXiv preprint arXiv:2210.03210, 2022 | 15 | 2022 |
| Alignment between initial state and mixer improves QAOA performance for constrained portfolio optimization Z He, R Shaydulin, S Chakrabarti, D Herman, C Li, Y Sun, M Pistoia CoRR, 2023 | 12 | 2023 |
Marco PistoiaHead of Global Technology Applied Research, Global Head of Quantum Computing, JPMorgan Chase & Co.Email verificata su jpmchase.com
