We study how efficiently a k-element set S\⊆[n] can be learned from a uniform superposition |S\⟩ of its elements. One can think of |S\⟩=\∑i\∈S|i\⟩/|S|\−\−\−\√ as the quantum version of a uniformly random sample over S, as in the classical analysis of the {\textquoteleft}{\textquoteleft}coupon collector problem.\&$\#$39;\&$\#$39; We show that if k is close to n, then we can learn S using asymptotically fewer quantum samples than random samples. In particular, if there are n\−k=O(1) missing elements then O(k) copies of |S\⟩ suffice, in contrast to the Θ(klogk) random samples needed by a classical coupon collector. On the other hand, if n\−k=Ω(k), then Ω(klogk) quantum samples are~necessary. More generally, we give tight bounds on the number of quantum samples needed for every k and n, and we give efficient quantum learning algorithms. We also give tight bounds in the model where we can additionally reflect through |S\⟩. Finally, we relate coupon collection to a known example separating proper and improper PAC learning that turns out to show no separation in the quantum case.

}, doi = {10.4230/LIPIcs.TQC.2020.10}, url = {https://arxiv.org/abs/2002.07688}, author = {Srinivasan Arunachalam and Aleksandrs Belovs and Andrew M. Childs and Robin Kothari and Ansis Rosmanis and Ronald de Wolf} }