Design and Training of Quantum Machine Learning Models for Noise Sensing and Phases of Matter Classification

Quantum Machine Learning (QML) aims to embed learning algorithms into the quantum computing framework. On one hand, QML can lead to exponential speedups for classical learning tasks, and on the other hand, it enables the use of data-driven approaches to study purely quantum problems. In the first part of this work training and designing problems of a particular QML application known as Quantum Generative Adversarial Networks are tackled, whereas in the last part a general framework dubbed Geometric Quantum Machine Learning is introduced. The latter leverages the symmetries of the tasks being solved to design quantum learning models that are easier to train and provide better performance.