Variational quantum approximate support vector machine with inference transfer

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 co...

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Veröffentlicht in:Scientific reports Jg. 13; H. 1; S. 3288 - 10
Hauptverfasser: Park, Siheon, Park, Daniel K., Rhee, June-Koo Kevin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 25.02.2023
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/1042
639/766/259
639/766/483/481
Algorithms
Bias
Classification
Computers
Datasets
Humanities and Social Sciences
Machine learning
multidisciplinary
Random access memory
Science
Science (multidisciplinary)
Support vector machines
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-29495-y