Single-qubit quantum classifier based on gradient-free optimization algorithm

Abstract

A single-qubit quantum classifier (SQC) based on a gradient-free optimization (GFO) algorithm, named the GFO-based SQC, is proposed to overcome the effects of barren plateaus caused by quantum devices. Here, a rotation gate RX (ϕ) is applied on the single-qubit binary quantum classifier, and the training data and parameters are loaded into ϕ in the form of vector multiplication. The cost function is decreased by finding the value of each parameter that yields 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 GFO-based SQC is demonstrated for classification tasks in Iris and MNIST datasets and compared with the Adam-based SQC and the quantum support vector machine (QSVM). Furthermore, the performance of the GFO-based SQC is discussed when the rotation gate in the quantum device is under different types of noise. The simulation results show that the GFO-based SQC can reach a high accuracy in reduced time. Additionally, the proposed GFO algorithm can quickly complete the training process of the SQC. Importantly, the GFO-based SQC has a good performance in noisy environments.