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arXiv 2404.03585quant-ph
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自由费米子态的PAC学习是NP难的

PAC-learning of free-fermionic states is NP-hard

Lennart Bittel, Antonio A. Mele, Jens Eisert, Lorenzo Leone

AI总结:

本研究证明判定数据集是否与自由费米子态一致是NP完全问题,进而表明该类经典可模拟量子态难以被PAC学习,为首个具有此性质的可模拟态类别。

摘要:

自由费米子态,也称为匹配门态或高斯态,是一类基本的量子态,因其高效的经典可模拟性以及在物理学各个领域中的关键作用而备受关注。随着量子设备的发展,实验现在能够从量子态中获取数据,包括期望值的估计。我们证明,判定一个由少量马约拉纳关联函数估计构成的数据集是否与某个自由费米子态一致,是一个NP完全问题。我们的结果也扩展到由泡利期望值估计构成的数据集。这与稳定子态的情况形成鲜明对比,在稳定子态中,类似问题可以高效求解。此外,我们的结果直接意味着自由费米子态在计算上难以被正确地进行PAC学习,其中量子态的PAC学习是Aaronson引入的一个学习框架。值得注意的是,这是第一类被证明具有此性质的经典可模拟量子态。

英文摘要:

Free-fermionic states, also known as matchgates or Gaussian states, are a fundamental class of quantum states due to their efficient classical simulability and their crucial role across various domains of Physics. With the advent of quantum devices, experiments now yield data from quantum states, including estimates of expectation values. We establish that deciding whether a given dataset, formed by a few Majorana correlation functions estimates, can be consistent with a free-fermionic state is an NP-complete problem. Our result also extends to datasets formed by estimates of Pauli expectation values. This is in stark contrast to the case of stabilizer states, where the analogous problem can be efficiently solved. Moreover, our results directly imply that free-fermionic states are computationally hard to properly PAC-learn, where PAC-learning of quantum states is a learning framework introduced by Aaronson. Remarkably, this is the first class of classically simulable quantum states shown to have this property.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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