Quantum Classifiers with Trainable Kernel

TL;DR

This paper introduces a trainable quantum kernel framework that enhances quantum classifiers' accuracy and efficiency, employing a universal feature mapping, improved SVM, and a multi-classifier system, validated through simulations.

Contribution

It presents a novel trainable quantum feature mapping layout, an improved quantum SVM, and a multi-classifier framework, advancing quantum classification methods.

Findings

Trainable quantum feature mapping shows strong clustering performance.

Classification accuracy surpasses existing quantum classifiers.

Numerical demonstrations validate the proposed methods in Qiskit.

Abstract

Kernel function plays a crucial role in machine learning algorithms such as classifiers. In this paper, we aim to improve the classification performance and reduce the reading out burden of quantum classifiers. We devise a universally trainable quantum feature mapping layout to broaden the scope of feature states and avoid the inefficiently straight preparation of quantum superposition states. We also propose an improved quantum support vector machine that employs partially evenly weighted trial states. In addition, we analyze its error sources and superiority. As a promotion, we propose a quantum iterative multi-classifier framework for one-versus-one and one-versus-rest approaches. Finally, we conduct corresponding numerical demonstrations in the \textit{qiskit} package. The simulation result of trainable quantum feature mapping shows considerable clustering performance, and the subsequent classification performance is superior to the existing quantum classifiers in terms of accuracy and distinguishability.