Shared Address Learning for Amplitude Encoding in Quantum Machine Learning

Quantum machine learning offers expressive Hilbert-space models, but loading classical data involves a trade-off between compactness and cost. Amplitude encoding is highly compact, yet state preparation can consume much of the entangling-gate budget on near-term hardware. This cost depends heavily on the address, which assigns data coordinates to computational-basis states and is typically fixed to a default order. Sharing one address across a dataset preserves all inner products and the ideal amplitude kernel, allowing us to reshape entanglement for efficient preparation. We prove an exponential fidelity separation between addresses on a family of maximally entangled states under limited gate budgets, and show that a correlation-based construction makes the family completely separable. Shared Address Learning (SAL) builds candidate addresses from the correlation structure of unlabeled data, refines them with a lightweight structural score, and selects one by shallow-circuit fidelity. Learned once, the address is applied to all inputs as classical preprocessing compatible with any loader. For images and pretrained vision and language features, SAL reaches equivalent preparation fidelity with significantly fewer entangling gates and consistently improves tensor-network and adaptive-compilation loaders. These gains extend to quantum kernel and quantum neural network (QNN) classifiers in simulation and persist for a QNN deployed on a superconducting processor.