17 citations · 27 across the 16 of their papers we have counts for
23 papers
Fourier Symmetrization for Geometric Quantum Machine Learning
Letao Wang, Abdel Lisser, Sreejith Sreekumar +1
Geometric quantum machine learning incorporates symmetry into quantum models, but how symmetry shapes their expressivity and guides effective model design remains insufficiently un…
Information Bottleneck under Perfect Privacy
Junle Zhong, Mohamad Assaad, Sreejith Sreekumar
In this work, we study the information bottleneck under perfect privacy, with particular emphasis on the active-rate regime, where the representation-rate constraint is binding and…
Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Letao Wang, Abdel Lisser, Sreejith Sreekumar +1
Partial differential equations (PDEs) play a crucial role in financial mathematics, particularly in portfolio optimization, and solving them using classical numerical or neural net…
Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings
Sreejith Sreekumar, Nir Weinberger
Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models. Here, we study a quantum version of this task for a simplified data model consistin…
Performance Guarantees for Quantum Neural Estimation of Entropies
Sreejith Sreekumar, Ziv Goldfeld, Mark M. Wilde
Estimating quantum entropies and divergences is an important problem in quantum physics, information theory, and machine learning. Quantum neural estimators (QNEs), which utilize a…
Deviation Inequalities for Rényi Divergence Estimators via Variational Expression
Sreejith Sreekumar, Kengo Kato
Rényi divergences play a pivotal role in information theory, statistics, and machine learning. While several estimators of these divergences have been proposed in the literature wi…
One-shot Multiple Access Channel Simulation
Aditya Nema, Sreejith Sreekumar, Mario Berta
We consider the problem of shared randomness-assisted multiple access channel (MAC) simulation for product inputs and characterize the one-shot communication cost region via almost…
Distributed Quantum Hypothesis Testing under Zero-rate Communication Constraints
Sreejith Sreekumar, Christoph Hirche, Hao-Chung Cheng +1
The trade-offs between error probabilities in quantum hypothesis testing are by now well-understood in the centralized setting, but much less is known for distributed settings. Her…
Locally-Measured Rényi Divergences
Tobias Rippchen, Sreejith Sreekumar, Mario Berta
We propose an extension of the classical Rényi divergences to quantum states through an optimization over probability distributions induced by restricted sets of measurements. In p…
Limit Distribution Theory for Quantum Divergences
Sreejith Sreekumar, Mario Berta
Estimation of quantum relative entropy and its Rényi generalizations is a fundamental statistical task in quantum information theory, physics, and beyond. While several estimators…
Quantum Neural Estimation of Entropies
Ziv Goldfeld, Dhrumil Patel, Sreejith Sreekumar +1
Entropy measures quantify the amount of information and correlation present in a quantum system. In practice, when the quantum state is unknown and only copies thereof are availabl…
Limit distribution theory for -Divergences
Sreejith Sreekumar, Ziv Goldfeld, Kengo Kato
-divergences, which quantify discrepancy between probability distributions, are ubiquitous in information theory, machine learning, and statistics. While there are numerous meth…
Neural Estimation of Statistical Divergences
Sreejith Sreekumar, Ziv Goldfeld
Statistical divergences (SDs), which quantify the dissimilarity between probability distributions, are a basic constituent of statistical inference and machine learning. A modern m…
Non-Asymptotic Performance Guarantees for Neural Estimation of -Divergences
Sreejith Sreekumar, Zhengxin Zhang, Ziv Goldfeld
Statistical distances (SDs), which quantify the dissimilarity between probability distributions, are central to machine learning and statistics. A modern method for estimating such…
Communicate to Learn at the Edge
Deniz Gunduz, David Burth Kurka, Mikolaj Jankowski +3
Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research cha…
The Secrecy Capacity of Cost-Constrained Wiretap Channels
Sreejith Sreekumar, Alexander Bunin, Ziv Goldfeld +2
In many information-theoretic channel coding problems, adding an input cost constraint to the operational setup amounts to restricting the optimization domain in the capacity formu…
Strong Converse for Testing Against Independence over a Noisy channel
Sreejith Sreekumar, Deniz Gündüz
A distributed binary hypothesis testing (HT) problem over a noisy (discrete and memoryless) channel studied previously by the authors is investigated from the perspective of the st…
Distributed Hypothesis Testing over a Noisy Channel: Error-exponents Trade-off
Sreejith Sreekumar, Deniz Gündüz
A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to …
Privacy-aware Distributed Hypothesis Testing
Sreejith Sreekumar, Asaf Cohen, Deniz Gündüz
A distributed binary hypothesis testing (HT) problem involving two parties, a remote observer and a detector, is studied. The remote observer has access to a discrete memoryless so…
Distributed Hypothesis Testing Over Discrete Memoryless Channels
Sreejith Sreekumar, Deniz Gündüz
A distributed binary hypothesis testing (HT) problem involving two parties, one referred to as the observer and the other as the detector is studied. The observer observes a discre…
Distributed Hypothesis Testing Over Noisy Channels
Sreejith Sreekumar, Deniz Gündüz
A distributed binary hypothesis testing problem, in which multiple observers transmit their observations to a detector over noisy channels, is studied. Given its own side informati…
Distributed Scheduling in Multiple Access with Bursty Arrivals and Delay Constraints
Sakshi Kapoor, Sreejith Sreekumar, Sibi Raj B Pillai
A multiple access system with bursty data arrivals to the terminals is considered. The users are frame-synchronized, with variable sized packets independently arriving in each slot…
Distributed Rate Adaptation and Power Control in Fading Multiple Access Channels
Sreejith Sreekumar, Bikash K. Dey, Sibi Raj B. Pillai
Traditionally, the capacity region of a coherent fading multiple access channel (MAC) is analyzed in two popular contexts. In the first, a centralized system with full channel stat…