ISSN :2582-9793

Variational Quantum Linear Solver Enhanced Quantum Support Vector Machine

Original Research (Published On: 13-Apr-2024 )
Variational Quantum Linear Solver Enhanced Quantum Support Vector Machine

Jianming YI

Adv. Artif. Intell. Mach. Learn., 4 (2):2164-2187

Jianming YI : Image Processing Department, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, 67663, Germany

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Article History: Received on: 07-Feb-24, Accepted on: 13-Mar-24, Published on: 13-Apr-24

Corresponding Author: Jianming YI


Citation: Jianming YI, Kalyani Suresh , Ali Moghiseh, Norbert Wehn, (2024). Variational Quantum Linear Solver Enhanced Quantum Support Vector Machine. Adv. Artif. Intell. Mach. Learn., 4 (2 ):2164-2187



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 effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions. Furthermore, we exploit both classical and quantum computing for various subroutines of our algorithm, and effectively mitigate 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.


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