Quantum data loading from the learned shared structure of real signals

Excerpt

arXiv:2610.06076v2 Announce Type: replace-cross Abstract: Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show that the signals of a real dataset share structure that can be learned once and reused. Our quantum-native loader learns a low-dimensional description of a dataset and prepares every signal with one fixed circuit set by a few numbers. Across seven views of five public datasets it meets the t