对比量子机器学习中编码方式对分类准确率的影响。
Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy
- 比较幅度编码与角度编码在量子电路中的表现。
- 相同架构下最高与最低准确率相差达41%。
- 编码方式是影响性能的关键超参数，适合量子算法研究者。
近年来，量子计算与机器学习的进展推动了量子机器学习（QML）的发展，旨在利用量子计算范式构建机器学习模型。其中广泛使用的是变分量子电路（VQC），一种混合模型：量子电路负责数据推断，经典优化器调整电路参数。量子电路包含编码层（将数据加载到电路中）和作为变换模板的量子线路（ansatz），负责处理数据。本文分析了幅度编码与角度编码两种策略，并考察旋转门类型对分类性能的影响。在Wine和Diabetes两个数据集上训练并评估不同模型，结果表明，在相同模型结构下，最佳与最差模型的准确率差异为10%至30%，最高可达41%。研究还揭示，编码中使用的旋转门选择显著影响分类表现，证实嵌入方式是变分量子电路的重要超参数。
原文摘要 · Abstract (English)
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.
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