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Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers. (arXiv:2102.08949v1 [quant-ph])

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.



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