  Quantum machine learning (QML) is a promising field that explores the
applications of quantum computing to machine learning tasks. A significant
hurdle in the advancement of quantum machine learning lies in the development
of efficient and resilient quantum classifiers capable of accurately mapping
input data to specific, discrete target outputs. In this paper, we propose a
novel approach to improve quantum classifier performance by using a data
re-uploading strategy. Re-uploading classical information into quantum states
multiple times can enhance the accuracy of quantum classifiers. We investigate
the effects of different cost functions, such as fidelity and trace distance,
on the optimization process and the classification results. We demonstrate our
approach to two classification patterns: a linear classification pattern (LCP)
and a non-linear classification pattern (NLCP). We evaluate the efficacy of our
approach by benchmarking it against four distinct optimization techniques:
L-BFGS-B, COBYLA, Nelder-Mead, and SLSQP. Additionally, we study the different
impacts of fixed datasets and random datasets. Our results show that our
approach can achieve high classification accuracy and robustness and outperform
the existing quantum classifier models.
