Shouvanik
Chakrabarti
Chakrabarti, S. ., Childs, A. M., Hung, S.-H. ., Li, T. ., Wang, C. ., & Wu, X. . (2023). Quantum Algorithm for Estimating Volumes of Convex Bodies. ACM Transactions on Quantum Computing, 4, 1–60. http://doi.org/10.1145/3588579 (Original work published May 2023)
You, X. ., Chakrabarti, S. ., Chen, B. ., & Wu, X. . (2023). Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. ArXiv. Retrieved from https://arxiv.org/abs/2303.14844 (Original work published March 2023)
Shi, K. ., Herrman, R. ., Shaydulin, R. ., Chakrabarti, S. ., Pistoia, M. ., & Larson, J. . (2022). Multiangle QAOA Does Not Always Need All Its Angles. In 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) (pp. 414–419). IEEE. http://doi.org/10.1109/sec54971.2022.00062 (Original work published December 2022)
You, X. ., Chakrabarti, S. ., & Wu, X. . (2022). A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers. ArXiv. Retrieved from https://arxiv.org/abs/2205.12481 (Original work published May 2022)
Pistoia, M. ., Ahmad, S. ., Ajagekar, A. ., Buts, A. ., Chakrabarti, S. ., Herman, D. ., … Yalovetzky, R. . (2021). Quantum Machine Learning for Finance. ArXiv. Retrieved from https://arxiv.org/abs/2109.04298 (Original work published September 2021)
Chakrabarti, S. ., Krishnakumar, R. ., Mazzola, G. ., Stamatopoulos, N. ., Woerner, S. ., & Zeng, W. . (2021). A Threshold for Quantum Advantage in Derivative Pricing. Quantum, 5, 463. http://doi.org/10.22331/q-2021-06-01-463 (Original work published June 2021)
Li, T. ., Wang, C. ., Chakrabarti, S. ., & Wu, X. . (2021). Sublinear Classical and Quantum Algorithms for General Matrix Games. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 8465–8473). Association for the Advancement of Artificial Intelligence (AAAI). http://doi.org/10.1609/aaai.v35i10.17028 (Original work published May 2021)
Zhu, S. ., Hung, S.-H. ., Chakrabarti, S. ., & Wu, X. . (2020). On the principles of differentiable quantum programming languages. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (pp. 272–285). ACM. http://doi.org/10.1145/3385412.3386011 (Original work published June 2020)
Chakrabarti, S. ., Childs, A. M., Li, T. ., & Wu, X. . (2020). Quantum algorithms and lower bounds for convex optimization. Quantum, 4. http://doi.org/10.22331/q-2020-01-13-221 (Original work published January 2020)
Chakrabarti, S. ., Huang, Y. ., Li, T. ., Feizi, S. ., & Wu, X. . (2019). Quantum Wasserstein Generative Adversarial Networks. Advances in Neural Information Processing Systems (NIPS), 32. http://doi.org/https://papers.nips.cc/paper/8903-quantum-wasserstein-generative-adversarial-networks.pdf (Original work published October 2019)