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Publications

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Author Title Type [ Year(Desc)]
Filters: Author is Shouvanik Chakrabarti  [Clear All Filters]
2019
T. Li, Chakrabarti, S., and Wu, X., “Sublinear quantum algorithms for training linear and kernel-based classifiers”, Proceedings of the 36th International Conference on Machine Learning (ICML 2019) PMLR , vol. 97, pp. 3815-3824, 2019.
S. Chakrabarti, Huang, Y., Li, T., Feizi, S., and Wu, X., “Quantum Wasserstein Generative Adversarial Networks”, Advances in Neural Information Processing Systems (NIPS), vol. 32, 2019.
2020
T. Li, Wang, C., Chakrabarti, S., and Wu, X., “Sublinear classical and quantum algorithms for general matrix games”, To appear in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), 2020.
S. Chakrabarti, Childs, A. M., Li, T., and Wu, X., “Quantum algorithms and lower bounds for convex optimization”, Quantum, vol. 4, no. 221, 2020.
S. Zhu, Hung, S. - H., Chakrabarti, S., and Wu, X., “On the Principles of Differentiable Quantum Programming Languages”, 2020.
2021
S. Chakrabarti, Krishnakumar, R., Mazzola, G., Stamatopoulos, N., Woerner, S., and Zeng, W. J., “A Threshold for Quantum Advantage in Derivative Pricing”, Quantum, vol. 5, p. 463, 2021.
M. Pistoia, Ahmad, S. Farhan, Ajagekar, A., Buts, A., Chakrabarti, S., Herman, D., Hu, S., Jena, A., Minssen, P., Niroula, P., Rattew, A., Sun, Y., and Yalovetzky, R., “Quantum Machine Learning for Finance”, 2021.
2022
X. You, Chakrabarti, S., and Wu, X., “A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers”, 2022.
2023
X. You, Chakrabarti, S., Chen, B., and Wu, X., “Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels”, 2023.
S. Chakrabarti, Childs, A. M., Hung, S. - H., Li, T., Wang, C., and Wu, X., “Quantum algorithm for estimating volumes of convex bodies”, ACM Transactions on Quantum Computing, vol. 4, 2023.