Learning DNFs under product distributions via μ-biased quantum Fourier sampling
We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle. The current best classical algorithm runs in superpolynomial time. Our result extends the work by Bshouty and Jackson (1998) that proved that DNF formulae are efficient...
| 主要な著者: | , , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
Rinton Press
2019
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| 要約: | We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle. The current best classical algorithm runs in superpolynomial time. Our result extends the work by Bshouty and Jackson (1998) that proved that DNF formulae are efficiently learnable under the uniform distribution using a quantum example oracle. Our proof is based on a new quantum algorithm that efficiently samples the coefficients of a $\mu$--biased Fourier transform. |
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