变分量子分类器局部优化算法的性能评价
Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers
摘要：
本文研究了量子变分电路中局部优化器的性能和作用。我们研究了两个最流行的优化器的性能，并将其结果与一些流行的经典机器学习算法进行了比较。我们在研究中使用的经典算法是支持向量机（SVM），梯度提升（GB）和随机森林（RF）。这些进行了比较与变分量子分类器（VQC）使用两套本地优化器，即AQGD和COBYLA。对于VQC的实验，使用了IBM Quantum Experience和IBM Qiskit，而对于经典的机器学习模型，使用了sci-kit learn。结果表明，在有噪声的即时尺度量子机器上进行机器学习可以产生与经典机器相当的结果。对于我们的实验，我们使用了一个流行的餐厅情绪分析数据集。从该数据集中提取的特征，然后在应用PCA后将特征集减少为5个特征。使用EfficientSU 2变分电路，使用100个epoch和150个epoch训练量子ML模型。总体而言，训练了四个量子ML模型，训练了三个经典ML模型。使用标准评价指标（即准确度、精确度、召回率、F分数）评价训练模型的性能。在所有情况下，具有100个Epochs的基于AQGD优化器的模型的性能优于所有其他模型。它产生了77%的准确率和0.785的F分数，这是所有训练模型中最高的。
英文摘要：
In this paper, we have studied the performance and role of local optimizers in quantum variational circuits. We studied the performance of the two most popular optimizers and compared their results with some popular classical machine learning algorithms. The classical algorithms we used in our study are support vector machine (SVM), gradient boosting (GB), and random forest (RF). These were compared with a variational quantum classifier (VQC) using two sets of local optimizers viz AQGD and COBYLA. For experimenting with VQC, IBM Quantum Experience and IBM Qiskit was used while for classical machine learning models, sci-kit learn was used. The results show that machine learning on noisy immediate scale quantum machines can produce comparable results as on classical machines. For our experiments, we have used a popular restaurant sentiment analysis dataset. The extracted features from this dataset and then after applying PCA reduced the feature set into 5 features. Quantum ML models were trained using 100 epochs and 150 epochs on using EfficientSU2 variational circuit. Overall, four Quantum ML models were trained and three Classical ML models were trained. The performance of the trained models was evaluated using standard evaluation measures viz, Accuracy, Precision, Recall, F-Score. In all the cases AQGD optimizer-based model with 100 Epochs performed better than all other models. It produced an accuracy of 77% and an F-Score of 0.785 which were highest across all the trained models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
