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Quantum Hamiltonian embeddings unlock hypothesis classes beyond classical reach by encoding datasets into parameterized Hamiltonians optimized for ground-state classification.
Classical algorithms struggle to learn quantum dynamics efficiently, revealing a stark separation that underscores the power of quantum machine learning.