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
3 Pith papers cite this work. Polarity classification is still indexing.
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An adaptive shot-allocation rule that spends noisy kernel-estimation measurements on SVM decision-critical entries beats uniform allocation and can stop early.
For shot-budgeted quantum kernel learning, AQKA allocates shots as s_ij ∝ |g_ij| sqrt(K_ij(1−K_ij)) and reports up to +32 accuracy points over uniform, mainly under planted-sparse sensitivity.
citing papers explorer
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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
An adaptive shot-allocation rule that spends noisy kernel-estimation measurements on SVM decision-critical entries beats uniform allocation and can stop early.
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AQKA: Active Quantum Kernel Acquisition Under a Shot Budget
For shot-budgeted quantum kernel learning, AQKA allocates shots as s_ij ∝ |g_ij| sqrt(K_ij(1−K_ij)) and reports up to +32 accuracy points over uniform, mainly under planted-sparse sensitivity.