All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
Shot-frugal and robust quan- tum kernel classifiers, 2023
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Introduces geometric-sensitivity and active-set-instability signals to adaptively allocate measurements for kernel SVMs under Bernoulli noise, with theory and synthetic/quantum-kernel experiments showing improved margin and support-vector recovery.
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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.
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Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations
Introduces geometric-sensitivity and active-set-instability signals to adaptively allocate measurements for kernel SVMs under Bernoulli noise, with theory and synthetic/quantum-kernel experiments showing improved margin and support-vector recovery.