Sublinear quantum algorithms for training linear and kernel-based classifiers. - Tongyang Li; Shouvanik Chakrabarti; Xiaodi Wu 001 (ICML, 2019)

Quantum Wasserstein Generative Adversarial Networks. - Shouvanik Chakrabarti; Yiming Huang; Tongyang Li; Soheil Feizi; Xiaodi Wu 001 (NeurIPS, 2019)

Quantum algorithm for estimating volumes of convex bodies. - Shouvanik Chakrabarti; Andrew M. Childs; Shih-Han Hung; Tongyang Li; Chunhao Wang; Xiaodi Wu 001 (CoRR, 2019)

On the principles of differentiable quantum programming languages. - Shaopeng Zhu; Shih-Han Hung; Shouvanik Chakrabarti; Xiaodi Wu 001 (PLDI, 2020)

Quantum algorithms and lower bounds for convex optimization. - Shouvanik Chakrabarti; Andrew M. Childs; Tongyang Li; Xiaodi Wu 001 (Quantum, 2020)

Sublinear Classical and Quantum Algorithms for General Matrix Games. - Tongyang Li; Chunhao Wang; Shouvanik Chakrabarti; Xiaodi Wu 001 (AAAI, 2021)

ICCAD Special Session Paper: Quantum Variational Methods for Quantum Applications. - Shouvanik Chakrabarti; Xuchen You; Xiaodi Wu 001 (ICCAD, 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; et al. (ICCAD, 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; et al. (CoRR, 2021)

A Threshold for Quantum Advantage in Derivative Pricing. - Shouvanik Chakrabarti; Rajiv Krishnakumar; Guglielmo Mazzola; Nikitas Stamatopoulos; Stefan Woerner; William J. Zeng (Quantum, 2021)

Multiangle QAOA Does Not Always Need All Its Angles. - Kaiyan Shi; Rebekah Herrman; Ruslan Shaydulin; Shouvanik Chakrabarti; Marco Pistoia; Jeffrey Larson 001 (SEC, 2022)

A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers. - Xuchen You; Shouvanik Chakrabarti; Xiaodi Wu 001 (CoRR, 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)

Quantum Computing for Optimization and Machine Learning. - Shouvanik Chakrabarti (2022)

Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. - Xuchen You; Shouvanik Chakrabarti; Boyang Chen; Xiaodi Wu 001 (ICML, 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)

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 005; Jeffrey Larson 001; Danylo Lykov; Pierre Minssen; Yue Sun; Yuri Alexeev; Joan M. Dreiling; et al. (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 005; Charles Lim; Ju Li 003; et al. (CoRR, 2023)

Privacy-preserving quantum federated learning via gradient hiding. - Changhao Li; Niraj Kumar 005; Zhixin Song; Shouvanik Chakrabarti; Marco Pistoia (CoRR, 2023)

Code from "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 005; Jeffrey Larson 001; Danylo Lykov; Pierre Minssen; Yue Sun; Yuri Alexeev; Joan M. Dreiling; et al. (Zenodo, 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; et al. (Quantum, 2023)

Invited: Challenges and Opportunities of Quantum Optimization in Finance. - Zichang He; Shouvanik Chakrabarti; Dylan Herman; Niraj Kumar 005; Changhao Li; Pierre Minssen; Pradeep Niroula; Ruslan Shaydulin; Yue Sun; Shree Hari Sureshbabu; Romina Yalovetzky; Marco Pistoia (DAC, 2024)

Prospects of Privacy Advantage in Quantum Machine Learning. - Jamie Heredge; Niraj Kumar 005; Dylan Herman; Shouvanik Chakrabarti; Romina Yalovetzky; Shree Hari Sureshbabu; Changhao Li; Marco Pistoia (CoRR, 2024)

Generalized Short Path Algorithms: Towards Super-Quadratic Speedup over Markov Chain Search for Combinatorial Optimization. - Shouvanik Chakrabarti; Dylan Herman; Guneykan Ozgul; Shuchen Zhu; Brandon Augustino; Tianyi Hao 006; Zichang He; Ruslan Shaydulin; Marco Pistoia (CoRR, 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, 2024)

Applications of Certified Randomness. - Omar Amer; Shouvanik Chakrabarti; Kaushik Chakraborty; Shaltiel Eloul; Niraj Kumar 005; Charles Lim; Minzhao Liu; Pradeep Niroula; Yash Satsangi; Ruslan Shaydulin; Marco Pistoia (CoRR, 2025)

On Speedups for Convex Optimization via Quantum Dynamics. - Shouvanik Chakrabarti; Dylan Herman; Jacob Watkins; Enrico Fontana; Brandon Augustino; Junhyung Lyle Kim; Marco Pistoia (CoRR, 2025)

Certified randomness using a trapped-ion quantum processor. - 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; et al. (Nat., 2025)

A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values. - Tyler Chen; Akshay Seshadri; Mattia J. Villani; Pradeep Niroula; Shouvanik Chakrabarti; Archan Ray; Pranav Deshpande; Romina Yalovetzky; Marco Pistoia; Niraj Kumar 005 (CoRR, 2025)

A simple analysis of a quantum-inspired algorithm for solving low-rank linear systems. - Tyler Chen; Junhyung Lyle Kim; Archan Ray; Shouvanik Chakrabarti; Dylan Herman; Niraj Kumar 005 (CoRR, 2025)