1. Variational quantum approximate support vector machine with inference transfer.
- Author
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Park, Siheon, Park, Daniel K., and Rhee, June-Koo Kevin
- Subjects
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SUPPORT vector machines , *MACHINE tools , *QUANTUM computers , *PERFORMANCE standards , *KERNEL operating systems , *MACHINE learning - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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