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Improved separation between quantum and classical computers for sampling and functional tasks

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arxiv 2410.20935 v1 pith:ZFFEVJ7I submitted 2024-10-28 quant-ph cs.CC

classification quant-phcs.CC
keywords computersmathsfquantumclassicallevelhierarchypolynomialsampling
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

This paper furthers existing evidence that quantum computers are capable of computations beyond classical computers. Specifically, we strengthen the collapse of the polynomial hierarchy to the second level if: (i) Quantum computers with postselection are as powerful as classical computers with postselection ($\mathsf{PostBQP=PostBPP}$), (ii) any one of several quantum sampling experiments ($\mathsf{BosonSampling}$, $\mathsf{IQP}$, $\mathsf{DQC1}$) can be approximately performed by a classical computer (contingent on existing assumptions). This last result implies that if any of these experiment's hardness conjectures hold, then quantum computers can implement functions classical computers cannot ($\mathsf{FBQP\neq FBPP}$) unless the polynomial hierarchy collapses to its 2nd level. These results are an improvement over previous work which either achieved a collapse to the third level or were concerned with exact sampling, a physically impractical case. The workhorse of these results is a new technical complexity-theoretic result which we believe could have value beyond quantum computation. In particular, we prove that if there exists an equivalence between problems solvable with an exact counting oracle and problems solvable with an approximate counting oracle, then the polynomial hierarchy collapses to its second level, indeed to $\mathsf{ZPP^{NP}}$.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Spectral Born machines: classically trainable quantum generative models for discrete data

    quant-ph 2026-07 conditional novelty 6.0 of 10

    Spectral Born machines are Fourier-phase quantum generative models over Z_d^n that train classically via graph-spectral MMD and show reduced parameters plus apparent overfitting resistance on integer data.

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