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[2602.19644] Spectral Phase Encoding for Quantum Kernel Methods
Summary
The paper presents Spectral Phase Encoding (SPE) for quantum kernel methods, analyzing their robustness against noise and comparing performance with classical methods.
Why It Matters
Understanding the robustness of quantum kernel methods is crucial for advancing quantum machine learning, especially in noisy environments typical of near-term quantum computers. This research provides insights into how preprocessing techniques can enhance performance, which is vital for practical applications in this emerging field.
Key Takeaways
- SPE combines discrete Fourier transform with diagonal phase-only embedding for improved robustness in quantum kernels.
- DFT-based preprocessing shows the least degradation under noise compared to other quantum and classical methods.
- The study emphasizes the importance of structure-aligned preprocessing in enhancing quantum kernel performance.
Computer Science > Machine Learning arXiv:2602.19644 (cs) [Submitted on 23 Feb 2026] Title:Spectral Phase Encoding for Quantum Kernel Methods Authors:Pablo Herrero Gómez, Antonio Jimeno Morenilla, David Muñoz-Hernández, Higinio Mora Mora View a PDF of the paper titled Spectral Phase Encoding for Quantum Kernel Methods, by Pablo Herrero G\'omez and 3 other authors View PDF HTML (experimental) Abstract:Quantum kernel methods are promising for near-term quantum ma- chine learning, yet their behavior under data corruption remains insuf- ficiently understood. We analyze how quantum feature constructions degrade under controlled additive noise. We introduce Spectral Phase Encoding (SPE), a hybrid construc- tion combining a discrete Fourier transform (DFT) front-end with a diagonal phase-only embedding aligned with the geometry of diagonal quantum maps. Within a unified framework, we compare QK-DFT against alternative quantum variants (QK-PCA, QK-RP) and classi- cal SVM baselines under identical clean-data hyperparameter selection, quantifying robustness via dataset fixed-effects regression with wild cluster bootstrap inference across heterogeneous real-world datasets. Across the quantum family, DFT-based preprocessing yields the smallest degradation rate as noise increases, with statistically sup- ported slope differences relative to PCA and RP. Compared to classical baselines, QK-DFT shows degradation comparable to linear SVM and more stable than RBF SVM under matched tuning. H...
