Hybrid Multi-Strategy Quantum Embedding With Trainable Parameters for Multi-Class Classification

Quantum machine learning is strongly influenced by how classical data is embedded into quantum circuits, yet most methods rely on fixed, task-agnostic encodings that cannot adapt to problem structure. This chapter introduces the Hybrid Multi-Strategy Quantum Embedding (HMQE) framework, which integrates angle encoding, parametric entanglement, and data re-uploading into a unified, trainable architecture. Unlike static embeddings, HMQE jointly optimizes embedding and circuit parameters using gradi