In a quantum PAC-learning model where examples are labeled quantum states, the VC-dimension alone does not determine sample complexity; new bounds based on quantum state distinguishability are derived.
Learning dnf over the uniform distribution using a quantum example oracle,
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Analysis of the sample complexity for PAC-learning functions defined over quantum states
In a quantum PAC-learning model where examples are labeled quantum states, the VC-dimension alone does not determine sample complexity; new bounds based on quantum state distinguishability are derived.