改进数据嵌入可显著提升量子自编码器的异常检测能力
The role of data embedding in quantum autoencoders for improved anomaly detection
- 对比三种数据嵌入方式，优化嵌入策略提升模型表现
- 简单变分电路配合优嵌入，异常检测准确率明显提高
- 适用于低维与高维复杂数据，适合量子机器学习研究者
量子自编码器（QAE）在异常检测任务中的表现高度依赖于数据嵌入方式和变分电路设计。本研究探讨了三种数据嵌入技术——数据重载、并行嵌入与交替嵌入——对QAE表示能力和检测效果的影响。结果表明，即使采用相对简单的变分电路，优化的数据嵌入策略也能显著提升异常检测准确率，并增强对不同数据集的底层结构表征能力。从低维玩具数据开始，通过可视化展示不同嵌入方式对模型可表示性的影响；随后扩展至高维复杂数据集，验证了嵌入方法对QAE性能的关键作用。
原文摘要 · Abstract (English)
The performance of Quantum Autoencoders (QAEs) in anomaly detection tasks is critically dependent on the choice of data embedding and ansatz design. This study explores the effects of three data embedding techniques, data re-uploading, parallel embedding, and alternate embedding, on the representability and effectiveness of QAEs in detecting anomalies. Our findings reveal that even with relatively simple variational circuits, enhanced data embedding strategies can substantially improve anomaly detection accuracy and the representability of underlying data across different datasets. Starting with toy examples featuring low-dimensional data, we visually demonstrate the effect of different embedding techniques on the representability of the model. We then extend our analysis to complex, higher-dimensional datasets, highlighting the significant impact of embedding methods on QAE performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。