Quantum Hamiltonian embeddings provide a theoretically grounded quantum machine learning framework with provable generalization guarantees and evidence of potential quantum advantage, demonstrated on real quantum processors.
This paper introduces quantum Hamiltonian embeddings, a supervised learning framework that encodes training data into a parameterized quantum Hamiltonian whose ground state performs classification.