Derives analytic formulae for curvature, volume forms, and harmonic maps on the induced Riemannian manifold of special unitary operators arising from quantum feature maps applied to data point clouds assumed to form smooth manifolds.
Supervised quantum machine learning models are kernel methods, 2021
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A review synthesizing foundations, constructions, advantage conditions, and challenges for non-variational quantum kernel methods in supervised learning.
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Geodesics of Quantum Feature Maps on the Space of Quantum Operators
Derives analytic formulae for curvature, volume forms, and harmonic maps on the induced Riemannian manifold of special unitary operators arising from quantum feature maps applied to data point clouds assumed to form smooth manifolds.
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Non-variational supervised quantum kernel methods: a review
A review synthesizing foundations, constructions, advantage conditions, and challenges for non-variational quantum kernel methods in supervised learning.