Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers

TL;DR

This study compares local optimizers in variational quantum classifiers with classical machine learning algorithms, demonstrating that quantum models with certain optimizers can achieve competitive accuracy on a sentiment analysis task.

Contribution

It provides an empirical comparison of local optimizers in quantum classifiers against classical models, highlighting the effectiveness of AQGD in noisy quantum environments.

Findings

AQGD optimizer outperformed others with 77% accuracy

Quantum models achieved results comparable to classical models

Quantum classifiers can be effective on noisy quantum hardware

Abstract

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.