  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.
