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arXiv Machine Learning By Ravi Kumar Jha, Hsuan-Yin Lin, Eirik Rosnes

Can Entanglement-Induced Feature-Map Designs Improve Projected Quantum Kernels?

Read the original on arXiv Machine Learning →

The paper investigates projected quantum kernels (PQKs) and how their performance is often obscured by extensive classical hyperparameter tuning of RBF kernels and SVM regularization. By comparing untuned and tuned PQKs to an RBF baseline, the authors isolate the native inductive bias of PQKs and find that recent designs can be improved by quantum feature maps that use two‑qubit entanglement to capture multivariate correlations in IoT data. The study shows that entanglement‑based topologies provide a stronger native inductive bias, leading to better PQK performance that stems from quantum‑mechanical properties rather than from classical tuning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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