Variational Quantum Approximate Support Vector Machine with Inference Transfer

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.

arXiv comment16 pages, 4 figures

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Cited by in corpus (5)

• Enhancing Quantum Support Vector Machines through Variational Kernel Training
• Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
• Neural quantum kernels: training quantum kernels with quantum neural networks
• Quantum inspired qubit qutrit neural networks for real time financial forecasting
• Multi-channel convolutional neural quantum embedding