Niraj Kumar

According to our database, Niraj Kumar authored at least 24 papers between 2019 and 2025.

Collaborative distances:

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

2025

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning.

Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray, Mattia J. Villani, Marco Pistoia, Niraj Kumar

CoRR, June, 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

CoRR, June, 2025

GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches.

Tyler Chen, Pradeep Niroula, Archan Ray, Pragna Subrahmanya, Marco Pistoia, Niraj Kumar

CoRR, June, 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, 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

Nat., April, 2025

Provably faster randomized and quantum algorithms for k-means clustering via uniform sampling.

Tyler Chen, Archan Ray, Akshay Seshadri, Dylan Herman, Bao Bach, Pranav Deshpande, Abhishek Som, Niraj Kumar, Marco Pistoia

CoRR, April, 2025

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks.

Georgios Papadopoulos, Shaltiel Eloul, Yash Satsangi, Jamie Heredge, Niraj Kumar, Chun-Fu Chen, Marco Pistoia

CoRR, April, 2025

New Improvements in Solving Large LABS Instances Using Massively Parallelizable Memetic Tabu Search.

Zhiwei Zhang, Jiayu Shen, Niraj Kumar, Marco Pistoia

CoRR, April, 2025

Applications of Certified Randomness.

Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty, Shaltiel Eloul, Niraj Kumar, Charles Lim, Minzhao Liu, Pradeep Niroula, Yash Satsangi, Ruslan Shaydulin, Marco Pistoia

CoRR, March, 2025

Des-q: a quantum algorithm to provably speedup retraining of decision trees.

Niraj Kumar, Romina Yalovetzky, Changhao Li, Pierre Minssen, Marco Pistoia

Quantum, 2025

2024

QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest.

Romina Yalovetzky, Niraj Kumar, Changhao Li, Marco Pistoia

CoRR, 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

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, 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

Dataset, August, 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

Expressive variational quantum circuits provide inherent privacy in federated learning.

Niraj Kumar, Jamie Heredge, Changhao Li, Shaltiel Eloul, Shree Hari Sureshbabu, Marco Pistoia

CoRR, 2023

Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification.

Niraj Kumar, Romina Yalovetzky, Changhao Li, Pierre Minssen, 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

2022

Graph neural network initialisation of quantum approximate optimisation.

Nishant Jain, Brian Coyle, Elham Kashefi, Niraj Kumar

Quantum, September, 2022

2021

On the Connection Between Quantum Pseudorandomness and Quantum Hardware Assumptions.

Mina Doosti, Niraj Kumar, Elham Kashefi, Kaushik Chakraborty

CoRR, 2021

2020

Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis.

Brian Coyle, Mina Doosti, Elham Kashefi, Niraj Kumar

CoRR, 2020

Quantum versus Classical Generative Modelling in Finance.

Brian Coyle, Maxwell Henderson, Justin Chan Jin Le, Niraj Kumar, Marco Paini, Elham Kashefi

CoRR, 2020

Client-Server Identification Protocols with Quantum PUF.

Mina Doosti, Niraj Kumar, Mahshid Delavar, Elham Kashefi

CoRR, 2020

2019

Practically Feasible Robust Quantum Money with Classical Verification.

Niraj Kumar

Cryptogr., 2019