Metric Learning with Quantum Feature Spaces
The Quantum Projective Metric Learning (QPMeL) framework learns quantum encodings by mapping classical data to surfaces of unit spheres in R3, producing polar angles that align with unentangled qubit states. It introduces a novel Projective Metric Function (PMeF) for similarity computation and a gradient trick to ensure stable classical training, enabling high-performance quantum embeddings efficiently.