  In the paper, a gradient-free optimization algorithm for single-qubit quantum
classifier is proposed to overcome the effects of barren plateau caused by
quantum devices. A rotation gate RX({\phi}) is applied on a single-qubit binary
quantum classifier, and the training data and parameters are loaded into {\phi}
with the form of vector-multiplication. The cost function is decreased by
finding the value of each parameter that yield the minimum expectation value of
measuring the quantum circuit. The algorithm is performed iteratively for all
parameters one by one, until the cost function satisfies the stop condition.
The proposed algorithm is demonstrated for a classification task and is
compared with that using Adam optimizer. Furthermore, the performance of the
single-qubit quantum classifier with the proposed gradient-free optimization
algorithm is discussed when the rotation gate in quantum device is under
different noise. The simulation results show that the single-qubit quantum
classifier with proposed gradient-free optimization algorithm can reach a high
accuracy faster than that using Adam optimizer. Moreover, the proposed
gradient-free optimization algorithm can quickly completes the training process
of the single-qubit classifier. Additionally, the single-qubit quantum
classifier with proposed gradient-free optimization algorithm has a good
performance in noisy environments.
