Shouvanik Chakrabarti

43 papers A* 6A 2Misc 1Journal 33

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Digital signatures with classical shadows on near-term quantum computers. CoRR Pradeep Niroula, Minzhao Liu, Sivaprasad Omanakuttan, David Amaro, Shouvanik Chakrabarti, Soumik Ghosh, Zichang He, Yuwei Jin, Fatih Kaleoglu, Steven Kordonowy, Rohan Kumar, Michael A. Perlin, Akshay Seshadri, Matthew Steinberg, Joseph Sullivan, Jacob Watkins, Henry Yuen, Ruslan Shaydulin
2026	J	`jnl`	Quantum Speedups for Derivative Pricing Beyond Black-Scholes. CoRR Dylan Herman, Yue Sun, Jin-Peng Liu, Marco Pistoia, Charlie C. L. Wang, Rob Otter, Shouvanik Chakrabarti, Aram W. Harrow
2026	J	`jnl`	Quantum Speedups for Group Relaxations of Integer Linear Programs. CoRR Brandon Augustino, Dylan Herman, Guneykan Ozgul, Jacob Watkins, Atithi Acharya, Enrico Fontana, Junhyung Lyle Kim, Shouvanik Chakrabarti
2025	J	`jnl`	A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values. CoRR Tyler Chen, Akshay Seshadri, Mattia J. Villani, Pradeep Niroula, Shouvanik Chakrabarti, Archan Ray, Pranav Deshpande, Romina Yalovetzky, Marco Pistoia, Niraj Kumar
2025	J	`jnl`	A simple analysis of a quantum-inspired algorithm for solving low-rank linear systems. CoRR Tyler Chen, Junhyung Lyle Kim, Archan Ray, Shouvanik Chakrabarti, Dylan Herman, Niraj Kumar
2025	J	`jnl`	Applications of Certified Randomness. CoRR Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty, Shaltiel Eloul, Niraj Kumar, Charles Lim, Minzhao Liu, Pradeep Niroula, Yash Satsangi, Ruslan Shaydulin, Marco Pistoia
2025	J	`jnl`	Certified randomness using a trapped-ion quantum processor. CoRR Minzhao Liu, Ruslan Shaydulin, Pradeep Niroula, Matthew DeCross, Shih-Han Hung, Wen Yu Kon, Enrique Cervero Martín, Kaushik Chakraborty, Omar Amer, Scott Aaronson, Atithi Acharya, Yuri Alexeev, K. Jordan Berg, Shouvanik Chakrabarti, Florian J. Curchod, Joan M. Dreiling, Neal Erickson, Cameron Foltz, Michael Foss-Feig, David Hayes, Travis S. Humble, Niraj Kumar, Jeffrey Larson, Danylo Lykov, Michael Mills, Steven A. Moses, Brian Neyenhuis, Shaltiel Eloul, Peter Siegfried, James Walker, Charles Lim, Marco Pistoia
2025	J	`jnl`	Certified randomness using a trapped-ion quantum processor. Nat. Minzhao Liu, Ruslan Shaydulin, Pradeep Niroula, Matthew DeCross, Shih-Han Hung, Wen Yu Kon, Enrique Cervero Martín, Kaushik Chakraborty, Omar Amer, Scott Aaronson, Atithi Acharya, Yuri Alexeev, K. Jordan Berg, Shouvanik Chakrabarti, Florian J. Curchod, Joan M. Dreiling, Neal Erickson, Cameron Foltz, Michael Foss-Feig, David Hayes, Travis S. Humble, Niraj Kumar, Jeffrey Larson, Danylo Lykov, Michael Mills, Steven A. Moses, Brian Neyenhuis, Shaltiel Eloul, Peter Siegfried, James Walker, Charles Lim, Marco Pistoia
2025	J	`jnl`	Mechanisms for Quantum Advantage in Global Optimization of Nonconvex Functions. CoRR Dylan Herman, Guneykan Ozgul, Anuj Apte, Junhyung Lyle Kim, Anupam Prakash, Jiayu Shen, Shouvanik Chakrabarti
2025	J	`jnl`	On Speedups for Convex Optimization via Quantum Dynamics. CoRR Shouvanik Chakrabarti, Dylan Herman, Jacob Watkins, Enrico Fontana, Brandon Augustino, Junhyung Lyle Kim, Marco Pistoia
2024	J	`jnl`	Generalized Short Path Algorithms: Towards Super-Quadratic Speedup over Markov Chain Search for Combinatorial Optimization. CoRR Shouvanik Chakrabarti, Dylan Herman, Guneykan Ozgul, Shuchen Zhu, Brandon Augustino, Tianyi Hao, Zichang He, Ruslan Shaydulin, Marco Pistoia
2024	A*	`conf`	Invited: Challenges and Opportunities of Quantum Optimization in Finance. DAC 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
2024	J	`jnl`	Parameter Setting in Quantum Approximate Optimization of Weighted Problems. Quantum Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, Marco Pistoia
2024	J	`jnl`	Prospects of Privacy Advantage in Quantum Machine Learning. CoRR Jamie Heredge, Niraj Kumar, Dylan Herman, Shouvanik Chakrabarti, Romina Yalovetzky, Shree Hari Sureshbabu, Changhao Li, Marco Pistoia
2023	J	`jnl`	Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Portfolio Optimization. CoRR Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti, Dylan Herman, Changhao Li, Yue Sun, Marco Pistoia
2023	A*	`conf`	Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. ICML Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu
2023	J	`jnl`	Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. CoRR Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu
2023	J	`jnl`	Blind quantum machine learning with quantum bipartite correlator. CoRR 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
2023	J	`jnl`	Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem. CoRR 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
2023	J	`jnl`	Privacy-preserving quantum federated learning via gradient hiding. CoRR Changhao Li, Niraj Kumar, Zhixin Song, Shouvanik Chakrabarti, Marco Pistoia
2023	J	`jnl`	Quantum Algorithm for Estimating Volumes of Convex Bodies. ACM Trans. Quantum Comput. Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung, Tongyang Li, Chunhao Wang, Xiaodi Wu
2023	J	`jnl`	Quantum Deep Hedging. CoRR 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
2023	J	`jnl`	Quantum Deep Hedging. Quantum 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
2022	J	`jnl`	A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers. CoRR Xuchen You, Shouvanik Chakrabarti, Xiaodi Wu
2022	Misc	`conf`	Multiangle QAOA Does Not Always Need All Its Angles. SEC Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Jeffrey Larson
2022	J	`jnl`	Numerical evidence against advantage with quantum fidelity kernels on classical data. CoRR Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Sami Khairy, Stefan M. Wild
2022	—	`—`	Quantum Computing for Optimization and Machine Learning. Shouvanik Chakrabarti
2021	J	`jnl`	A Threshold for Quantum Advantage in Derivative Pricing. Quantum Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng
2021	A	`conf`	ICCAD Special Session Paper: Quantum Variational Methods for Quantum Applications. ICCAD Shouvanik Chakrabarti, Xuchen You, Xiaodi Wu
2021	A	`conf`	Quantum Machine Learning for Finance ICCAD Special Session Paper. ICCAD 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
2021	J	`jnl`	Quantum Machine Learning for Finance. CoRR 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
2021	A*	`conf`	Sublinear Classical and Quantum Algorithms for General Matrix Games. AAAI Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu
2020	J	`jnl`	A Threshold for Quantum Advantage in Derivative Pricing. CoRR Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng
2020	J	`jnl`	On the Principles of Differentiable Quantum Programming Languages. CoRR Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu
2020	A*	`conf`	On the principles of differentiable quantum programming languages. PLDI Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu
2020	J	`jnl`	Quantum algorithms and lower bounds for convex optimization. Quantum Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, Xiaodi Wu
2020	J	`jnl`	Sublinear classical and quantum algorithms for general matrix games. CoRR Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu
2019	A*	`conf`	Quantum Wasserstein Generative Adversarial Networks. NeurIPS Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu
2019	J	`jnl`	Quantum Wasserstein Generative Adversarial Networks. CoRR Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu
2019	J	`jnl`	Quantum algorithm for estimating volumes of convex bodies. CoRR Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung, Tongyang Li, Chunhao Wang, Xiaodi Wu
2019	A*	`conf`	Sublinear quantum algorithms for training linear and kernel-based classifiers. ICML Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
2019	J	`jnl`	Sublinear quantum algorithms for training linear and kernel-based classifiers. CoRR Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
2018	J	`jnl`	Quantum algorithms and lower bounds for convex optimization. CoRR Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, Xiaodi Wu