Xiao-Ying Zhang | Searcharxiv

arXiv2024

Quantum Bayes Classifiers and Their Application in Image Classification

Bayesian networks are powerful tools for probabilistic analysis and have been widely used in machine learning and data science. Unlike the time-consuming parameter training process of neural networks, Bayes classifiers constructed on Bayesian networks can make decisions based solely on statistical data from samples. In this paper, we focus on constructing quantum Bayes classifiers (QBCs). We design both a naive QBC and three semi-naive QBCs (SN-QBCs). These QBCs are then applied to image classification tasks. To reduce computational complexity, we employ a local feature sampling method to extract a limited number of feature attributes from an image. These attributes serve as nodes of the Bayesian networks to generate the QBCs. We simulate these QBCs on the MindQuantum platform and evaluate their performance on the MNIST and Fashion-MNIST datasets. Our results demonstrate that these QBCs achieve good classification accuracies even with a limited number of attributes. The classification accuracies of QBCs on the MNIST dataset surpass those of classical Bayesian networks and quantum neural networks that utilize all available feature attributes.

quant-ph↗

arXiv2022

An efficient combination strategy for hybird quantum ensemble classifier

Quantum machine learning has shown advantages in many ways compared to classical machine learning. In machine learning, a difficult problem is how to learn a model with high robustness and strong generalization ability from a limited feature space. Combining multiple models as base learners, ensemble learning (EL) can effectively improve the accuracy, generalization ability, and robustness of the final model. The key to EL lies in two aspects, the performance of base learners and the choice of the combination strategy. Recently, quantum EL (QEL) has been studied. However, existing combination strategies in QEL are inadequate in considering the accuracy and variance among base learners. This paper presents a hybrid EL framework that combines quantum and classical advantages. More importantly, we propose an efficient combination strategy for improving the accuracy of classification in the framework. We verify the feasibility and efficiency of our framework and strategy by using the MNIST dataset. Simulation results show that the hybrid EL framework with our combination strategy not only has a higher accuracy and lower variance than the single model without the ensemble, but also has a better accuracy than the majority voting and the weighted voting strategies in most cases.

quant-ph↗