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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 $O(k^{1.5}(log k)^2)$ uniform quantum examples for that function. This improves over the bound of $widetilde{Theta}(kn)$ uniformly random classical examples (Haviv and Regev, CCC15). Our main tool is an improvement of Changs lemma for the special case of sparse functions. Second, we show that if a concept class $mathcal{C}$ can be exactly learned using $Q$ quantum membership queries, then it can also be learned using $Oleft(frac{Q^2}{log Q}log|mathcal{C}|right)$ classical membership queries. This improves the previous-best simulation result (Servedio and Gortler, SICOMP04) by a $log Q$-factor.
We propose a learning model called the quantum statistical learning QSQ model, which extends the SQ learning model introduced by Kearns to the quantum setting. Our model can be also seen as a restriction of the quantum PAC learning model: here, the l
In the exact quantum query model a successful algorithm must always output the correct function value. We investigate the function that is true if exactly $k$ or $l$ of the $n$ input bits given by an oracle are 1. We find an optimal algorithm (for so
This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning from membership queries, and Probably Approximately Corre
$ ewcommand{eps}{varepsilon} $In learning theory, the VC dimension of a concept class $C$ is the most common way to measure its richness. In the PAC model $$ ThetaBig(frac{d}{eps} + frac{log(1/delta)}{eps}Big) $$ examples are necessary and sufficien
We generalize the PAC (probably approximately correct) learning model to the quantum world by generalizing the concepts from classical functions to quantum processes, defining the problem of emph{PAC learning quantum process}, and study its sample co