Shouvanik Chakrabarti

According to our database, Shouvanik Chakrabarti authored at least 23 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Parameter Setting in Quantum Approximate Optimization of Weighted Problems.

Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, Marco Pistoia

Quantum, January, 2024

Prospects of Privacy Advantage in Quantum Machine Learning.

Jamie Heredge, Niraj Kumar, Dylan Herman, Shouvanik Chakrabarti, Romina Yalovetzky, Shree Hari Sureshbabu, Changhao Li, Marco Pistoia

CoRR, 2024

Invited: Challenges and Opportunities of Quantum Optimization in Finance.

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

Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

2023

Quantum Deep Hedging.

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, November, 2023

Privacy-preserving quantum federated learning via gradient hiding.

Changhao Li, Niraj Kumar, Zhixin Song, Shouvanik Chakrabarti, Marco Pistoia

CoRR, 2023

Blind quantum machine learning with quantum bipartite correlator.

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

CoRR, 2023

Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem.

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

CoRR, 2023

Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Portfolio Optimization.

Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti, Dylan Herman, Changhao Li, Yue Sun, Marco Pistoia

CoRR, 2023

Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels.

Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu

Proceedings of the International Conference on Machine Learning, 2023

2022

Quantum Computing for Optimization and Machine Learning.

Shouvanik Chakrabarti

PhD thesis, 2022

Numerical evidence against advantage with quantum fidelity kernels on classical data.

Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Sami Khairy, Stefan M. Wild

CoRR, 2022

A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers.

Xuchen You, Shouvanik Chakrabarti, Xiaodi Wu

CoRR, 2022

Multiangle QAOA Does Not Always Need All Its Angles.

Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Jeffrey Larson

Proceedings of the 7th IEEE/ACM Symposium on Edge Computing, 2022

2021

A Threshold for Quantum Advantage in Derivative Pricing.

Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng

Quantum, 2021

Quantum Machine Learning for Finance.

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

CoRR, 2021

Quantum Machine Learning for Finance ICCAD Special Session Paper.

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

Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021

ICCAD Special Session Paper: Quantum Variational Methods for Quantum Applications.

Shouvanik Chakrabarti, Xuchen You, Xiaodi Wu

Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021

Sublinear Classical and Quantum Algorithms for General Matrix Games.

Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Quantum algorithms and lower bounds for convex optimization.

Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, Xiaodi Wu

Quantum, 2020

On the principles of differentiable quantum programming languages.

Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu

Proceedings of the 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2020

2019

Quantum algorithm for estimating volumes of convex bodies.

Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung, Tongyang Li, Chunhao Wang, Xiaodi Wu

CoRR, 2019

Quantum Wasserstein Generative Adversarial Networks.

Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sublinear quantum algorithms for training linear and kernel-based classifiers.

Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu

Proceedings of the 36th International Conference on Machine Learning, 2019