Variational Quantum Approximate Support Vector Machine with Inference Transfer
This work addresses the challenge of making quantum machine learning practical for complex data classification on current noisy quantum hardware, though it appears incremental as it builds on existing kernel-based quantum classifiers.
The authors tackled the problem of implementing quantum machine learning on near-term quantum computers by proposing a Variational Quantum Approximate Support Vector Machine algorithm, which demonstrated sub-quadratic runtime complexity and was tested on toy, Iris, and MNIST datasets as proof of concept.
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.