Measurement-Budget Allocation in Quantum Learning with Finite-Shot Generalization Guarantees

arXiv cs.AIAugust 27, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2608.24891v1 Announce Type: new Abstract: On near-term quantum hardware, estimating a Born probability requires repeated circuit executions. A quantum learning experiment with a fixed measurement budget $B$ must therefore decide how many distinct training states $n$ to use and how many shots $S$ to allocate to each state. We study this tradeoff for binary quantum classifiers with fixed or independently selected measurement operators $M$, where the ideal score is $\Tr(M\rho)$. We prove a distribution-free generalization bound that separates the finite-sample and finite-shot contributions. The sample term scales as $\sqrt{d/n}$, while the shot term scales as $\sqrt{(\log n)/S}$; under th

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