7 papers
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…
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…
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…