Variational Quantum Approximate Support Vector Machine with Inference Transfer

2022-06-29 · Siheon Park, Daniel K. Park, June-Koo Kevin Rhee

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.

📄 PDF Abstract BibTeX arXiv:2206.14507

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Classification Quantum Machine Learning

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