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Many classes of Boolean functions can be tested much faster than they can be learned. However, this speedup tends to rely on query access to the function f. When access is limited to random samples (x, f(x))—the passive testing model and a natural setting for data science—testing can become much harder. Here we introduce "quantum passive testing" as a quantum version of this “data science scenario”: quantum algorithms that test properties of a function f solely from quantum data in the form of copies of the function state |f⟩ ∝ \sum_x |x,f(x)⟩. Just like classical samples, function states are independent of the property of interest and can be collected well in advance. Our work is an exploration of this property testing model under various guises.

https://combalgo.labri.fr/pmwiki.php/Groupe/Info-Quantique

room 178