Quantum machine learning for multiclass classification beyond kernel methods

TL;DR

This paper introduces a quantum algorithm with multiple quantum kernels that significantly improves multiclass classification performance and demonstrates quantum advantage over classical methods in real-world applications.

Contribution

It presents a novel quantum algorithm with six quantum kernels specifically designed for multiclass classification, surpassing classical methods in efficiency and accuracy.

Findings

Quantum algorithm outperforms classical in six real-world multiclass tasks.

Quantum kernels enhance data mapping into quantum state spaces.

Quantum approach achieves better generalization and classification accuracy.

Abstract

Quantum machine learning is considered one of the current research fields with immense potential. In recent years, Havl\'i\v{c}ek et al. [Nature 567, 209-212 (2019)] have proposed a quantum machine learning algorithm with quantum-enhanced feature spaces, which effectively addressed a binary classification problem on a superconducting processor and offered a potential pathway to achieving quantum advantage. However, a straightforward binary classification algorithm falls short in solving multiclass classification problems. In this paper, we propose a quantum algorithm that rigorously demonstrates that quantum kernel methods enhance the efficiency of multiclass classification in real-world applications, providing quantum advantage. To demonstrate quantum advantage, we design six distinct quantum kernels within the quantum algorithm to map input data into quantum state spaces and estimate the corresponding quantum kernel matrices. The results from quantum simulations reveal that the quantum algorithm outperforms its classical counterpart in handling six real-world multiclass classification problems. Furthermore, we leverage a variety of performance metrics to comprehensively evaluate the classification and generalization performance of the quantum algorithm. The results demonstrate that the quantum algorithm achieves superior classification and better generalization performance relative to classical counterparts.