@Article{jqc.2021.016390,
AUTHOR = {Muhammad Haris, Muhammad Noman Hasan
, Abdul Basit, Shiyin Qin},
TITLE = {Lifetime Prediction of LiFePO4 Batteries Using Multilayer Classical-Quantum  Hybrid Classifier},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {89--95},
URL = {http://www.techscience.com/jqc/v3n3/46038},
ISSN = {2579-0145},
ABSTRACT = { This article presents a multilayer hybrid classical-quantum classifier for 
predicting the lifetime of LiFePO4 batteries using early degradation data. The 
multilayer approach uses multiple variational quantum circuits in cascade, which 
allows more parameters to be used as weights in a single run hence increasing 
accuracy and provides faster cost function convergence for the optimizer. The 
proposed classifier predicts with an accuracy of 92.8% using data of the first four 
cycles. The effectiveness of the hybrid classifier is also presented by validating the 
performance using untrained data with an accuracy of 84%. We also demonstrate 
that the proposed classifier outperforms traditional machine learning algorithms in 
classification accuracy. In this paper, we show the application of quantum machine 
learning in solving a practical problem. This study will help researchers to apply 
quantum machine learning algorithms to more complex real-world applications, 
and reducing the gap between quantum and classical computing.},
DOI = {10.32604/jqc.2021.016390}
}