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arXiv · 2206.14507

Variational Quantum Approximate Support Vector Machine with Inference Transfer

Siheon Park·Daniel K. Park·June-Koo Kevin Rhee
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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.

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Siheon Park, Daniel K. Park, June-Koo Kevin Rhee. 2022-06-29. Variational Quantum Approximate Support Vector Machine with Inference Transfer. https://doi.org/10.1038/s41598-023-29495-y

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