用量子电路+神经网络生成真实表格数据，隐私受限时表现更优
QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesis
- 量子电路学复杂分布，再由神经网络映射为表格特征
- 分类任务上性能比现有模型最高提升54.07%
- 适合数据少或隐私敏感场景的合成数据生成
由于特征类型多样且维度高，生成真实表格数据极具挑战。我们提出QTabGAN，一种用于表格数据合成的混合量子-经典生成对抗框架。该模型特别适用于真实数据稀缺或受隐私限制的场景。通过利用量子电路的表达能力学习复杂数据分布，并使用经典神经网络将结果映射到表格特征。我们在多个分类和回归数据集上评估QTabGAN，与最先进的生成模型对比。实验表明，QTabGAN在各类分类数据集和评估指标上最高提升达54.07%，验证了其在表格数据合成中的可扩展性，凸显了量子辅助生成建模的潜力。
原文摘要 · Abstract (English)
Synthesizing realistic tabular data is challenging due to heterogeneous feature types and high dimensionality. We introduce QTabGAN, a hybrid quantum-classical generative adversarial framework for tabular data synthesis. QTabGAN is especially designed for settings where real data are scarce or restricted by privacy constraints. The model exploits the expressive power of quantum circuits to learn complex data distributions, which are then mapped to tabular features using classical neural networks. We evaluate QTabGAN on multiple classification and regression datasets and benchmark it against leading state-of-the-art generative models. Experiments show that QTabGAN achieves up to 54.07% improvement across various classification datasets and evaluation metrics, thus establishing a scalable quantum approach to tabular data synthesis and highlighting its potential for quantum-assisted generative modelling.
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