Gradient-Free optimization algorithm for single-qubit quantum classifier

TL;DR

This paper introduces a gradient-free optimization algorithm for single-qubit quantum classifiers that overcomes barren plateau issues, achieves faster high-accuracy training, and performs well under noisy conditions.

Contribution

The paper proposes a novel gradient-free optimization method tailored for single-qubit quantum classifiers, improving training speed and noise robustness compared to traditional optimizers.

Findings

Faster convergence to high accuracy than Adam optimizer.

Effective in noisy quantum environments.

Reduces impact of barren plateaus in quantum training.

Abstract

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.