Shouvanik Chakrabarti

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 29

Top Topics

Special Session

Quantum Computing

Portfolio Optimization

Machine Learning

Top Venues

Publications

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 (2024)
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 (2024)
Changhao Li, Niraj Kumar, Zhixin Song, Shouvanik Chakrabarti, Marco Pistoia
Privacy-preserving quantum federated learning via gradient hiding. CoRR (2023)
Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. CoRR (2023)
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 (2023)
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 (2023)
Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. ICML (2023)
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia
Quantum Deep Hedging. Quantum 7 (2023)
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia
Quantum Deep Hedging. CoRR (2023)
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 (2023)
Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Jeffrey Larson
Multiangle QAOA Does Not Always Need All Its Angles. SEC (2022)
Xuchen You, Shouvanik Chakrabarti, Xiaodi Wu
A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers. CoRR (2022)
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 (2022)
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)
Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng
A Threshold for Quantum Advantage in Derivative Pricing. Quantum 5 (2021)
Shouvanik Chakrabarti, Xuchen You, Xiaodi Wu
ICCAD Special Session Paper: Quantum Variational Methods for Quantum Applications. ICCAD (2021)
Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu
Sublinear Classical and Quantum Algorithms for General Matrix Games. AAAI (2021)
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 (2021)
Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, Xiaodi Wu
Quantum algorithms and lower bounds for convex optimization. Quantum 4 (2020)
Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu
On the principles of differentiable quantum programming languages. PLDI (2020)
Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu
On the Principles of Differentiable Quantum Programming Languages. CoRR (2020)
Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu
Sublinear classical and quantum algorithms for general matrix games. CoRR (2020)
Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng
A Threshold for Quantum Advantage in Derivative Pricing. CoRR (2020)
Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu
Quantum Wasserstein Generative Adversarial Networks. CoRR (2019)
Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung, Tongyang Li, Chunhao Wang, Xiaodi Wu
Quantum algorithm for estimating volumes of convex bodies. CoRR (2019)
Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
Sublinear quantum algorithms for training linear and kernel-based classifiers. CoRR (2019)
Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu
Quantum Wasserstein Generative Adversarial Networks. NeurIPS (2019)
Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
Sublinear quantum algorithms for training linear and kernel-based classifiers. ICML (2019)
Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, Xiaodi Wu
Quantum algorithms and lower bounds for convex optimization. CoRR (2018)