Variational Quantum Approximate Support Vector Machine with Inference Transfer

TL;DR

This paper introduces VQASVM, a quantum machine learning algorithm that achieves faster classification with feasible quantum operations on NISQ computers, demonstrated through experiments on toy and standard datasets.

Contribution

The paper presents a novel variational quantum SVM algorithm with sub-quadratic runtime suitable for NISQ devices, advancing quantum machine learning scalability.

Findings

Empirical demonstration of VQASVM on toy datasets on cloud NISQ machines.

Numerical results on Iris and MNIST datasets showing scalability.

Sub-quadratic runtime complexity achieved with quantum operations.

Abstract

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.