Variational Quantum Approximate Support Vector Machine with Inference Transfer

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.

16 pages, 4 figures

References in corpus (5)

• Quantum algorithm for solving linear systems of equations
• Quantum random access memory
• Generalization in quantum machine learning from few training data
• The theory of the quantum kernel-based binary classifier
• Compact quantum kernel-based binary classifier

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