# Quantum models learn complexity via data prioritization > Source: > Published: 2026-07-07 04:00:00+00:00 A paper by **Erik Recio-Armengol** and coauthors, **arXiv:2411.11954** (quant-ph), revised **6 Jul 2026** and published in **Phys. Rev. Research 8, 033006 (2026)**, proposes a training framework that ranks and paces quantum machine learning training samples by how informative they are, adapting classical curriculum learning and hard-example-mining techniques to quantum models. The paper reports theoretical arguments and numerical experiments on recognition tasks for quantum phases of matter, and frames the approach as complementary to warm-start initialization rather than a replacement for it. For ML and quantum practitioners, data-centric curricula and sample prioritization are a low-cost way to introduce inductive bias during training, which can ease optimization for high-dimensional quantum models without changing circuit hardware.