Do quantum computers need to encode all your data to win?

Quantum machine learning usually requires loading an entire dataset into a quantum superposition — an expensive and noisy process. This work proves that encoding only the most informative subset of variables into quantum states is enough to retain a learning advantage, as long as that quantum slice captures patterns the classical part misses. A 64-qubit example under realistic noise confirms the advantage survives polynomial-cost encoding, offering a practical path for near-term quantum hardware.