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Proper vs Improper Quantum PAC learning
In the classical case, there are examples of concept classes with VC dimension $d$ that have sample complexity $\Omega\left(\frac d\epsilon\log\frac1\epsilon\right)$ for proper learning with error $\epsilon$, while the complexity for improper learning is O$\!\left(\frac d\epsilon\right)$.
Optimal lower bounds for Quantum Learning via Information Theory
In this paper, we derive optimal lower bounds for quantum sample complexity in both the PAC and agnostic models via an information-theoretic approach.
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