Open Access
ARTICLE
Lifetime Prediction of LiFePO4 Batteries Using Multilayer Classical-Quantum Hybrid Classifier
Muhammad Haris1,*, Muhammad Noman Hasan1
, Abdul Basit2, Shiyin Qin1
1 School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China
2 China Academy of Space Technology, Shenzhou Institute, Beijing, 100010, China
* Corresponding Author: Muhammad Haris. Email:
Journal of Quantum Computing 2021, 3(3), 89-95. https://doi.org/10.32604/jqc.2021.016390
Received 02 May 2021; Accepted 10 August 2021; Issue published 21 December 2021
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.
Keywords
Cite This Article
M. Haris, M. N. Hasan, A. Basit and S. Qin, "Lifetime prediction of lifepo4 batteries using multilayer classical-quantum hybrid classifier,"
Journal of Quantum Computing, vol. 3, no.3, pp. 89–95, 2021. https://doi.org/10.32604/jqc.2021.016390