标题： 评估变分量子机器学习中的角度和振幅编码策略：它们对模型准确性的影响 显示英文标题

标题： Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy

摘要： 最近在量子计算和机器学习方面的进展引起了对量子机器学习(QML)的关注，QML旨在通过利用量子计算范式来开发机器学习模型。 该领域中广泛使用的一种模型是变分量子电路(VQC)，这是一种混合模型，其中量子电路处理数据推理，而经典优化调整电路的参数。 量子电路包括一个编码层，用于将数据加载到电路中，以及一个模板电路，称为ansatz，负责处理数据。 本研究通过考虑幅度编码和角度编码模型进行分析，并检查应用的旋转门类型如何影响模型的分类性能。 这种比较是通过对两个数据集Wine和Diabetes中的不同模型进行训练并评估其性能来完成的。 研究表明，在相同的模型拓扑下，最佳模型和最差模型之间的准确率差异范围为10%到30%，差异最高可达41%。 此外，结果强调了编码中使用的旋转门的选择可以显著影响模型的分类性能。 这些发现确认了嵌入是VQC模型的一个超参数。 显示英文摘要

摘要： Recent advancements in Quantum Computing and Machine Learning have increased attention to Quantum Machine Learning (QML), which aims to develop machine learning models by exploiting the quantum computing paradigm. One of the widely used models in this area is the Variational Quantum Circuit (VQC), a hybrid model where the quantum circuit handles data inference while classical optimization adjusts the parameters of the circuit. The quantum circuit consists of an encoding layer, which loads data into the circuit, and a template circuit, known as the ansatz, responsible for processing the data. This work involves performing an analysis by considering both Amplitude- and Angle-encoding models, and examining how the type of rotational gate applied affects the classification performance of the model. This comparison is carried out by training the different models on two datasets, Wine and Diabetes, and evaluating their performance. The study demonstrates that, under identical model topologies, the difference in accuracy between the best and worst models ranges from 10% to 30%, with differences reaching up to 41%. Moreover, the results highlight how the choice of rotational gates used in encoding can significantly impact the model's classification performance. The findings confirm that the embedding represents a hyperparameter for VQC models.