Private learning implies quantum stability
Srinivasan Arunachalam, Yihui Quek, et al.
NeurIPS 2021
We present two new results about exact learning by quantum computers. First, we show how to exactly learn a k-Fourier-sparse n-bit Boolean function from O(k1.5(log k)2) uniform quantum examples for that function. This improves over the bound of Θ(e kn) uniformly random classical examples (Haviv and Regev, CCC'15). Additionally, we provide a possible direction to improve our Oe(k1.5) upper bound by proving an improvement of Chang's lemma for k-Fourier-sparse Boolean functions. Second, we show that if a concept class C can be exactly learned using Q quantum membership queries, then it can also be learned using O (logQ2Q log |C| ) classical membership queries. This improves the previous-best simulation result (Servedio and Gortler, SICOMP'04) by a log Q-factor.
Srinivasan Arunachalam, Yihui Quek, et al.
NeurIPS 2021
Srinivasan Arunachalam, Arkopal Dutt, et al.
ICALP 2024
Srinivasan Arunachalam
APS March Meeting 2024
Srinivasan Arunachalam, Louis Schatzki
STACS 2025