Hybrid Methods for Unsupervised Quantum Artificial Intelligence

Quantum computing exploits superposition, entanglement, and interference to address problems beyond classical reach, yet current devices are limited by noise and scale. Hybrid quantum–classical approaches therefore play a key role in achieving near-term utility by integrating quantum subroutines into classical workflows. Within this setting, Quantum Artificial Intelligence (QAI) offers new opportunities for machine learning. This thesis focuses on unsupervised Quantum Machine Learning, motivated by the abundance of unlabeled data and the importance of tasks such as clustering, dimensionality reduction, and anomaly detection. We study how to design hardware-efficient quantum subroutines, identify unsupervised paradigms well suited to hybrid computation, and assess potential advantages over classical methods. The contributions include a critical review of existing QAI approaches, the development of shallow-depth quantum subroutines for core operations, and the proposal of hybrid algorithms evaluated on synthetic and real datasets. The results highlight when quantum components provide practical value and clarify trade-offs between performance and complexity under current hardware constraints.