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Journal Articles Transactions on Machine Learning Research Journal Year : 2023

On Adaptivity in Quantum Testing

Abstract

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

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  • HAL Id : hal-04107265 , version 1

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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⟩
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