Variational Quantum Linear Solver enhanced Quantum Support Vector Machine

TL;DR

This paper introduces a novel quantum machine learning approach that enhances the scalability and efficiency of quantum support vector machines on NISQ devices by integrating a variational quantum linear solver.

Contribution

The paper presents a new VQLS-enhanced QSVM method that improves scalability, trainability, and runtime efficiency for quantum SVMs on NISQ hardware.

Findings

Successfully classified Iris dataset in up to 7-dimensional space

Achieved hyperplane separation in 8-dimensional feature space

Significant reduction in runtime for cost calculations

Abstract

Quantum Support Vector Machines (QSVM) play a vital role in using quantum resources for supervised machine learning tasks, such as classification. However, current methods are strongly limited in terms of scalability on Noisy Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a least squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset, which consists of three distinct iris plant species. Based on this, we explore the practicality and effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions. Furthermore, by strategically exploiting both classical and quantum computing for various subroutines of our algorithm, we effectively mitigate practical challenges associated with the implementation. These include significant improvement in the trainability of the variational ansatz and notable reductions in run-time for cost calculations. Based on the numerical experiments, our approach exhibits the capability of identifying a separating hyperplane in an 8-dimensional feature space. Moreover, it consistently demonstrated strong performance across various instances with the same dataset.