Quantum Machine Learning for Industrial Applications

The thesis presents advancements in Quantum Machine Learning (QML), focusing on addressing limitations of classical machine learning in industrial applications. Key contributions include the establishment of theoretical guarantees for the trainability of Hamming-weight preserving variational quantum circuits, the introduction of subspace-preserving QML algorithms such as photonic circuits and quantum convolutional neural networks, and a framework for analyzing variational quantum circuits as quantum Fourier models. These findings aim to enhance the practical applicability of quantum technologies, providing a pathway for achieving polynomial quantum advantages in real-world scenarios.