<ul><li>Geometric Machine Learning (GML) has shown improved performance by considering non-Euclidean geometry in data spaces.</li><li>Quantum Machine Learning (QML) leverages quantum state manifolds for learning tasks.</li><li>QML is a specialized branch of GML, with quantum states residing on curved manifolds.</li><li>Hybrid classical-quantum architectures demonstrate tangible benefits by combining classical feature extraction with quantum embeddings.</li></ul>

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

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