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.
Dequantizing algorithms to understand quantum advantage in machine learning
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All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
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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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New perspectives on quantum kernels through the lens of entangled tensor kernels
All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.