Quantum machine learning (QML) promises significant speedups, particularly when operating on quantum datasets. However, its progress is hindered by the scarcity of suitable training data. Existing synthetic data generation methods fall short in capturing essential entanglement properties, limiting their utility for QML. To address this, we introduce QuFoundry, a low-depth quantum data generation framework that produces entangled, high-quality samples emulating diverse classical and quantum distributions, enabling more effective development and evaluation of QML models in representative-data settings.
@article{arxiv.2509.21622,
title = {QuFoundry: Generating Data with Quantum Properties for Quantum Machine Learning Utility},
author = {Jason Ludmir and Ian Martin and Nicholas S. DiBrita and Tirthak Patel},
journal= {arXiv preprint arXiv:2509.21622},
year = {2026}
}