Muhammad Haris1,*, Muhammad Noman Hasan1
, Abdul Basit2, Shiyin Qin1
Journal of Quantum Computing, Vol.3, No.3, pp. 89-95, 2021, DOI:10.32604/jqc.2021.016390
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… More >
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