A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods

Geometric Machine Learning (GML) and Quantum Machine Learning (QML) leverage non-Euclidean geometry and quantum properties for advanced data analysis. GML excels in non-linear spaces, while QML boosts expressiveness through quantum states. Hybrid pipelines combining classical and quantum techniques offer benefits in specific tasks, aiming to advance machine intelligence across various applications. A study combines geometry and quantum computing for machine learning, focusing on non-Euclidean descriptors. It successfully processes complex medical imaging data, surpassing classical methods, highlighting the importance of geometric information in quantum models. The research suggests practical applications on quantum hardware, demonstrating the superiority of hybrid quantum-classical models.