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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

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

classification quant-ph cs.CC
keywords computersmathsfquantumclassicallevelhierarchypolynomialsampling
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 3 Pith papers

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

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

    quant-ph 2026-07 conditional novelty 6.0

    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.

  2. Qudit extension of parameterized IQP circuits: A generative quantum machine learning approach to integer data

    quant-ph 2026-06 unverdicted novelty 5.0

    Qudit extension of parameterized IQP circuits proposed for generative modeling of integer data, with loss function and covariance matrix, validated on electron shower energy deposits in CLIC electromagnetic calorimeter.

  3. IQPopt: Fast optimization of instantaneous quantum polynomial circuits in JAX

    quant-ph 2025-01 unverdicted novelty 5.0

    IQPopt is a JAX-based software tool enabling classical optimization of IQP circuits with thousands of qubits via efficient simulation of Pauli-Z expectation values, plus a module for quantum generative model training.