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ORCIDShouvanik Chakrabarti
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2020 – today
- 2024
[j4]Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, Marco Pistoia:
Parameter Setting in Quantum Approximate Optimization of Weighted Problems. Quantum 8: 1231 (2024)- [c9]Zichang He
, Shouvanik Chakrabarti, Dylan Herman, Niraj Kumar, Changhao Li, Pierre Minssen, Pradeep Niroula, Ruslan Shaydulin, Yue Sun, Shree Hari Sureshbabu, Romina Yalovetzky, Marco Pistoia:
Invited: Challenges and Opportunities of Quantum Optimization in Finance. DAC 2024: 362:1-362:4 - [i18]Jamie Heredge, Niraj Kumar, Dylan Herman, Shouvanik Chakrabarti, Romina Yalovetzky, Shree Hari Sureshbabu, Changhao Li, Marco Pistoia:
Prospects of Privacy Advantage in Quantum Machine Learning. CoRR abs/2405.08801 (2024) - [i17]Shouvanik Chakrabarti, Dylan Herman, Guneykan Ozgul, Shuchen Zhu, Brandon Augustino, Tianyi Hao, Zichang He, Ruslan Shaydulin, Marco Pistoia:
Generalized Short Path Algorithms: Towards Super-Quadratic Speedup over Markov Chain Search for Combinatorial Optimization. CoRR abs/2410.23270 (2024) - 2023
- [j3]El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Chun-Fu Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia:
Quantum Deep Hedging. Quantum 7: 1191 (2023) - [c8]Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu:
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. ICML 2023: 40199-40224 - [i16]Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu:
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. CoRR abs/2303.14844 (2023) - [i15]El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Chun-Fu Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia:
Quantum Deep Hedging. CoRR abs/2303.16585 (2023) - [i14]Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti, Dylan Herman, Changhao Li, Yue Sun, Marco Pistoia:
Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Portfolio Optimization. CoRR abs/2305.03857 (2023) - [i13]Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti, Matthew DeCross, Dylan Herman, Niraj Kumar, Jeffrey Larson, Danylo Lykov, Pierre Minssen, Yue Sun, Yuri Alexeev, Joan M. Dreiling, John P. Gaebler, Thomas M. Gatterman, Justin A. Gerber, Kevin Gilmore, Dan Gresh, Nathan Hewitt, Chandler V. Horst, Shaohan Hu, Jacob Johansen, Mitchell Matheny, Tanner Mengle, Michael Mills, Steven A. Moses, Brian Neyenhuis, Peter Siegfried, Romina Yalovetzky, Marco Pistoia:
Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem. CoRR abs/2308.02342 (2023) - [i12]Changhao Li, Boning Li, Omar Amer, Ruslan Shaydulin, Shouvanik Chakrabarti, Guoqing Wang, Haowei Xu, Hao Tang, Isidor Schoch, Niraj Kumar, Charles Lim, Ju Li, Paola Cappellaro, Marco Pistoia:
Blind quantum machine learning with quantum bipartite correlator. CoRR abs/2310.12893 (2023) - [i11]Changhao Li, Niraj Kumar, Zhixin Song, Shouvanik Chakrabarti, Marco Pistoia:
Privacy-preserving quantum federated learning via gradient hiding. CoRR abs/2312.04447 (2023) - 2022
- [b1]Shouvanik Chakrabarti:
Quantum Computing for Optimization and Machine Learning. University of Maryland, College Park, MD, USA, 2022 - [c7]Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Jeffrey Larson:
Multiangle QAOA Does Not Always Need All Its Angles. SEC 2022: 414-419 - [i10]Xuchen You, Shouvanik Chakrabarti, Xiaodi Wu:
A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers. CoRR abs/2205.12481 (2022) - [i9]Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Sami Khairy, Stefan M. Wild:
Numerical evidence against advantage with quantum fidelity kernels on classical data. CoRR abs/2211.16551 (2022) - 2021
- [j2]Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng:
A Threshold for Quantum Advantage in Derivative Pricing. Quantum 5: 463 (2021) - [c6]Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu:
Sublinear Classical and Quantum Algorithms for General Matrix Games. AAAI 2021: 8465-8473 - [c5]Shouvanik Chakrabarti, Xuchen You, Xiaodi Wu:
ICCAD Special Session Paper: Quantum Variational Methods for Quantum Applications. ICCAD 2021: 1-7 - [c4]Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur G. Rattew, Yue Sun, Romina Yalovetzky:
Quantum Machine Learning for Finance ICCAD Special Session Paper. ICCAD 2021: 1-9 - [i8]Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur G. Rattew, Yue Sun, Romina Yalovetzky:
Quantum Machine Learning for Finance. CoRR abs/2109.04298 (2021) - 2020
- [j1]Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, Xiaodi Wu:
Quantum algorithms and lower bounds for convex optimization. Quantum 4: 221 (2020) - [c3]Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu:
On the principles of differentiable quantum programming languages. PLDI 2020: 272-285 - [i7]Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu:
On the Principles of Differentiable Quantum Programming Languages. CoRR abs/2004.01122 (2020) - [i6]Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng:
A Threshold for Quantum Advantage in Derivative Pricing. CoRR abs/2012.03819 (2020) - [i5]Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu:
Sublinear classical and quantum algorithms for general matrix games. CoRR abs/2012.06519 (2020)
2010 – 2019
- 2019
- [c2]Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu:
Sublinear quantum algorithms for training linear and kernel-based classifiers. ICML 2019: 3815-3824 - [c1]Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. NeurIPS 2019: 6778-6789 - [i4]Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu:
Sublinear quantum algorithms for training linear and kernel-based classifiers. CoRR abs/1904.02276 (2019) - [i3]Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung, Tongyang Li, Chunhao Wang, Xiaodi Wu:
Quantum algorithm for estimating volumes of convex bodies. CoRR abs/1908.03903 (2019) - [i2]Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. CoRR abs/1911.00111 (2019) - 2018
- [i1]Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, Xiaodi Wu:
Quantum algorithms and lower bounds for convex optimization. CoRR abs/1809.01731 (2018)
Coauthor Index
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