Quantum kernels with Gaussian state encoding for machine learning

DOI10.1016/J.PHYSLETA.2022.128088zbMATH Open1495.81028OpenAlexW4221088362WikidataQ114141687 ScholiaQ114141687MaRDI QIDQ2133131

Yanyan Li

Publication date: 29 April 2022

Published in: Physics Letters. A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physleta.2022.128088

zbMATH Keywords

quantum machine learningcontinuous-variable quantum computing

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Hilbert and pre-Hilbert spaces: geometry and topology (including spaces with semidefinite inner product) (46C05) Quantum computation (81P68) Coherent states (81R30) Set functions and measures and integrals in infinite-dimensional spaces (Wiener measure, Gaussian measure, etc.) (28C20) Other nonclassical models of computation (68Q09)

Cites Work

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Related Items (6)

Kernel-based classification using quantum mechanics ⋮ Title not available (Why is that?) ⋮ The role of entanglement for enhancing the efficiency of quantum kernels towards classification ⋮ The theory of the quantum kernel-based binary classifier ⋮ Variable hyperparameterized Gaussian kernel using displaced squeezed vacuum state ⋮ Optimal Universal Learning Machines for Quantum State Discrimination

Uses Software

• Scikit
• PRMLT

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