66 citations · 145 across the 19 of their papers we have counts for
22 papers
From Quantum Tunneling to Classical Optimization through Instantons
Jiayu Shen, Dylan Herman, Guneykan Ozgul +5
We use an instanton-based analysis to show that Path Integral Stochastic Gradient Descent (PISGD), a classical optimization algorithm instantiated from path integral Monte Carlo (P…
Quantum-Informed Portfolio Selection: An End-to-End Pipeline Validated on Trapped-Ion Hardware with Real Market Data
Romina Yalovetzky, Martin J. A. Schuetz, Zichang He +11
Portfolio diversification - a cornerstone of modern investment management - can be formulated as a Maximum Independent Set (MIS) problem on asset correlation graphs. Solving this p…
Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
Mattia J. Villani, Pranav Deshpande, Akshay Seshadri +2
Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in high-stakes settings. Existing…
Anytime Training with Schedule-Free Spectral Optimization
Anuj Apte, Pranav Deshpande, Niraj Kumar +2
Standard neural network training relies on learning-rate schedules tied to a fixed horizon, leading to strong path dependence and costly re-tuning as data availability changes. Sch…
A simple analysis of a quantum-inspired algorithm for solving low-rank linear systems
Tyler Chen, Junhyung Lyle Kim, Archan Ray +3
We describe and analyze a simple algorithm for sampling from the solution to a linear system . We assume…
GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches
Tyler Chen, Pradeep Niroula, Archan Ray +3
A litany of theoretical and numerical results have established the sketch-and-precondition paradigm as a powerful approach to solving large linear regression problems in standard c…
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray +3
We present MetaTT, a Tensor Train (TT) adapter framework for fine-tuning of pre-trained transformers. MetaTT enables flexible and parameter-efficient model adaptation by using a si…
A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values
Tyler Chen, Akshay Seshadri, Mattia J. Villani +7
Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is diffic…
A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks
Georgios Papadopoulos, Shaltiel Eloul, Yash Satsangi +4
The loss landscape of Variational Quantum Neural Networks (VQNNs) is characterized by local minima that grow exponentially with increasing qubits. Because of this, it is more chall…
Provably faster randomized and quantum algorithms for -means clustering via uniform sampling
Tyler Chen, Archan Ray, Akshay Seshadri +6
The -means algorithm (Lloyd's algorithm) is a widely used method for clustering unlabeled data. A key bottleneck of the -means algorithm is that each iteration requires time…
New Improvements in Solving Large LABS Instances Using Massively Parallelizable Memetic Tabu Search
Zhiwei Zhang, Jiayu Shen, Niraj Kumar +1
Low Autocorrelation Binary Sequences (LABS) is a particularly challenging binary optimization problem which quickly becomes intractable in finding the global optimum for problem si…
Certified randomness using a trapped-ion quantum processor
Minzhao Liu, Ruslan Shaydulin, Pradeep Niroula +29
While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a…
Applications of Certified Randomness
Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty +8
Certified randomness can be generated with untrusted remote quantum computers using multiple known protocols, one of which has been recently realized experimentally. Unlike the ran…
Adaptive and Robust Watermark for Generative Tabular Data
Dung Daniel Ngo, Archan Ray, Akshay Seshadri +6
In recent years, watermarking generative tabular data has become a prominent framework to protect against the misuse of synthetic data. However, while most prior work in watermarki…
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest
Romina Yalovetzky, Niraj Kumar, Changhao Li +1
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data…
Prospects of Privacy Advantage in Quantum Machine Learning
Jamie Heredge, Niraj Kumar, Dylan Herman +5
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collab…
Quantum option pricing via the Karhunen-Loève expansion
Anupam Prakash, Yue Sun, Shouvanik Chakrabarti +8
We consider the problem of pricing discretely monitored Asian options over monitoring points where the underlying asset is modeled by a geometric Brownian motion. We provide tw…
Privacy-preserving quantum federated learning via gradient hiding
Changhao Li, Niraj Kumar, Zhixin Song +2
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed…
Blind quantum machine learning with quantum bipartite correlator
Changhao Li, Boning Li, Omar Amer +11
Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quan…
Expressive variational quantum circuits provide inherent privacy in federated learning
Niraj Kumar, Jamie Heredge, Changhao Li +3
Federated learning has emerged as a viable distributed solution to train machine learning models without the actual need to share data with the central aggregator. However, standar…
Des-q: a quantum algorithm to provably speedup retraining of decision trees
Niraj Kumar, Romina Yalovetzky, Changhao Li +2
Decision trees are widely adopted machine learning models due to their simplicity and explainability. However, as training data size grows, standard methods become increasingly slo…
The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze
Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti +5
Using tools from the representation theory of compact Lie groups, we formulate a theory of Barren Plateaus (BPs) for parameterized quantum circuits whose observables lie in their d…