An adaptive shot-allocation rule that spends noisy kernel-estimation measurements on SVM decision-critical entries beats uniform allocation and can stop early.
Quantum-efficient kernel target alignment
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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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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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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.