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Variational quantum approximate support vector machine with inference transfer
Siheon Park, D. Park, J. Rhee·June 29, 2022·DOI: 10.1038/s41598-023-29495-y
PhysicsComputer ScienceMedicine
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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.