On Adaptivity in Quantum Testing - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Transactions on Machine Learning Research Journal Year : 2023

On Adaptivity in Quantum Testing


Can adaptive strategies outperform non-adaptive ones for quantum hypothesis selection? We exhibit problems where adaptive strategies provably reduce the number of required samples by a factor four in the worst case, and possibly more when the actual difficulty of the problem makes it possible. In addition, we exhibit specific hypotheses classes for which there is a provable polynomial separation between adaptive and non-adaptive strategies-a specificity of the quantum framework that does not appear in classical testing.
Fichier principal
Vignette du fichier
On_adaptivity.pdf (494.14 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04107265 , version 1 (26-05-2023)




  • HAL Id : hal-04107265 , version 1


Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir. On Adaptivity in Quantum Testing. Transactions on Machine Learning Research Journal, 2023, pp.1-33. ⟨hal-04107265⟩
135 View
127 Download


Gmail Facebook X LinkedIn More