Practically Feasible Robust Quantum Money with Classical Verification. - Niraj Kumar 005 (Cryptogr., 2019)

Client-Server Identification Protocols with Quantum PUF. - Mina Doosti; Niraj Kumar 005; Mahshid Delavar; Elham Kashefi (CoRR, 2020)

Quantum versus Classical Generative Modelling in Finance. - Brian Coyle; Maxwell Henderson; Justin Chan Jin Le; Niraj Kumar 005; Marco Paini; Elham Kashefi (CoRR, 2020)

Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis. - Brian Coyle; Mina Doosti; Elham Kashefi; Niraj Kumar 005 (CoRR, 2020)

On the Connection Between Quantum Pseudorandomness and Quantum Hardware Assumptions. - Mina Doosti; Niraj Kumar 005; Elham Kashefi; Kaushik Chakraborty (CoRR, 2021)

Graph neural network initialisation of quantum approximate optimisation. - Nishant Jain; Brian Coyle; Elham Kashefi; Niraj Kumar 005 (Quantum, 2022)

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)

Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification. - Niraj Kumar 005; Romina Yalovetzky; Changhao Li; Pierre Minssen; Marco Pistoia (CoRR, 2023)

Expressive variational quantum circuits provide inherent privacy in federated learning. - Niraj Kumar 005; Jamie Heredge; Changhao Li; Shaltiel Eloul; Shree Hari Sureshbabu; 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 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)

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)

QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest. - Romina Yalovetzky; Niraj Kumar 005; Changhao Li; Marco Pistoia (CoRR, 2024)

Des-q: a quantum algorithm to provably speedup retraining of decision trees. - Niraj Kumar 005; Romina Yalovetzky; Changhao Li; Pierre Minssen; Marco Pistoia (Quantum, 2025)

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)

New Improvements in Solving Large LABS Instances Using Massively Parallelizable Memetic Tabu Search. - Zhiwei Zhang; Jiayu Shen; Niraj Kumar 005; Marco Pistoia (CoRR, 2025)

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks. - Georgios Papadopoulos 007; Shaltiel Eloul; Yash Satsangi; Jamie Heredge; Niraj Kumar 005; Chun-Fu Chen 001; Marco Pistoia (CoRR, 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 005; 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)

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 005 (CoRR, 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)

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 005 (CoRR, 2025)