2 citations · 2 across the 5 of their papers we have counts for
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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…
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…
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…
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…