对比多种量子编码方法在不同数据集上的表现，为选型提供依据
Benchmarking data encoding methods in Quantum Machine Learning
- 系统测试常见量子编码方法在多个数据集的表现
- 不同编码方法在不同数据集上性能差异显著
- 为实际应用选择合适编码方法提供实证参考
数据编码在量子机器学习（QML）中起着基础性且独特的作用。与经典方法直接处理向量数据不同，QML需通过编码电路将经典数据转换为量子态，即量子特征映射或量子嵌入。该步骤利用希尔伯特空间的高维性和非线性特性，可在复杂特征空间中实现比经典方法更高效的区分能力。然而，编码方式对模型性能影响显著，目前尚无通用规则指导如何根据数据集选择最佳编码。现有多种编码方法使用不同的量子逻辑门，我们研究了最常用的编码类型，并在不同数据集上进行了基准测试。
原文摘要 · Abstract (English)
Data encoding plays a fundamental and distinctive role in Quantum Machine Learning (QML). While classical approaches process data directly as vectors, QML may require transforming classical data into quantum states through encoding circuits, known as quantum feature maps or quantum embeddings. This step leverages the inherently high-dimensional and non-linear nature of Hilbert space, enabling more efficient data separation in complex feature spaces that may be inaccessible to classical methods. This encoding part significantly affects the performance of the QML model, so it is important to choose the right encoding method for the dataset to be encoded. However, this choice is generally arbitrary, since there is no "universal" rule for knowing which encoding to choose based on a specific set of data. There are currently a variety of encoding methods using different quantum logic gates. We studied the most commonly used types of encoding methods and benchmarked them using different datasets.
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。