QMill: Representative Quantum Data Generation for Quantum Machine Learning Utility
Jason Ludmir, Ian Martin, Nicholas S. DiBrita, Daniel Leeds, Tirthak Patel
Published: 2025/9/25
Abstract
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 QMill, 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.