Title: QMill: Representative Quantum Data Generation for Quantum Machine Learning Utility Show Chinese title

Title: QMill：量子机器学习效用的代表性量子数据生成

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. Show Chinese abstract

Abstract: 量子机器学习（QML）有望实现显著的加速，尤其是在处理量子数据集时。 然而，其进展受到合适训练数据稀缺的阻碍。 现有的合成数据生成方法在捕捉关键的纠缠特性方面存在不足，限制了它们在QML中的实用性。 为了解决这个问题，我们引入了QMill，这是一种低深度的量子数据生成框架，能够生成具有纠缠性的高质量样本，模拟各种经典和量子分布，在代表性数据设置中实现了更有效的QML模型开发和评估。