PoAPL
A trainable layer that encodes feature vectors onto 2 rotational coordinates $R_y(\theta),R_z(\gamma)$ for a unit sphere. Used to map data onto the Bloch Sphere surface for qubits
Quantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings