Quantum computing aims to leverage the principles of quantum mechanics, such as superposition, to encode and process information in ways that classical computers cannot, potentially handling exponentially larger amounts of information. However, harnessing this computational advantage requires quantum algorithms capable of encoding data into superpositions and providing answers with minimal...
Measuring the uncertainty associated to a model's prediction is a central part of statistical practice. In the context of modern deep learning practice, several methods for quantifying the uncertainty of neural networks co-exist. Yet, theoretical guarantees for these methods are scarce in the theoretical literature. In this talk, I will discuss how some of them compare in a mathematically...
