Entanglement unlocks scaling for quantum machine learning

New research by Los Alamos National Laboratory has overcome a potential hurdle in the practical implementation of quantum neural networks, effectively negating the previously assumed need for exponentially large training sets. The quantum No-Free-Lunch theorem reveals that quantum entanglement removes this exponential overhead, proving that both big data and big entanglement are valuable in quantum machine learning. This breakthrough suggests that quantum neural networks are moving towards outperforming classical counterparts.