Non-variational supervised quantum kernel methods: a review
Quantum kernel methods (QKMs) have emerged as a prominent framework for supervised quantum machine learning. Unlike variational quantum algorithms, which rely on gradient-based optimisation and may suffer from issues such as barren plateaus, non-variational QKMs employ fixed quantum feature maps, with model selection performed classically via convex optimisation and cross-validation. This separation of quantum feature embedding from classical training ensures stable optimisation while leveraging quantum circuits to encode data in high-dimensional Hilbert spaces. In this review, we provide a thorough analysis of non-variational supervised QKMs, covering their foundations in classical kerne...
| Authors | John Tanner, Chon-Fai Kam, Jingbo Wang |
|---|---|
| Primary category | quant-ph |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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