Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.20567 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911335541899264 |
|---|---|
| author | Micklethwaite, Emily Lowe, Adam |
| author_facet | Micklethwaite, Emily Lowe, Adam |
| contents | Radial basis function (RBF) networks are expanded to incorporate quantum kernel functions enabling a new type of hybrid quantum-classical machine learning algorithm. Using this approach, synthetic examples are introduced which allow for proof of concept on interpolation and classification applications. Quantum kernels have primarily been applied to support vector machines (SVMs), however the quantum kernel RBF network offers potential benefit over quantum kernel based SVMs due to the RBF networks ability to perform multi-class classification natively compared to the standard implementation of the SVM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classification using quantum kernels in a radial basis function network Micklethwaite, Emily Lowe, Adam Quantum Physics Radial basis function (RBF) networks are expanded to incorporate quantum kernel functions enabling a new type of hybrid quantum-classical machine learning algorithm. Using this approach, synthetic examples are introduced which allow for proof of concept on interpolation and classification applications. Quantum kernels have primarily been applied to support vector machines (SVMs), however the quantum kernel RBF network offers potential benefit over quantum kernel based SVMs due to the RBF networks ability to perform multi-class classification natively compared to the standard implementation of the SVM. |
| title | Classification using quantum kernels in a radial basis function network |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2512.20567 |