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arXiv 0908.2782quant-phcs.CC
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绝热量子优化在NP完全问题的随机实例中失败

Adiabatic quantum optimization fails for random instances of NP-complete problems

Boris Altshuler, Hari Krovi, Jeremie Roland

摘要:

绝热量子优化已经吸引了大量的关注,因为小规模的模拟给了希望,它将允许有效地解决NP完全问题。后来,否定的结果证明了绝热优化需要指数时间的特殊设计的硬实例的存在。尽管如此,对于NP完全问题的随机实例,这种情况仍有希望不会发生。这是一个重要的问题,因为随机实例是当前经典求解器无法解决的硬实例的良好模型,因此需要有效的量子算法。在这里,我们将表明,由于一个类似于安德森局域化的现象,一个指数小的本征值间隙出现在大的随机情况下,非常接近年底的算法的绝热哈密顿量的频谱。这意味着不幸的是,绝热量子优化也会因为陷入局部最小值而失败,除非计算是指数级的。

英文摘要:

Adiabatic quantum optimization has attracted a lot of attention because small scale simulations gave hope that it would allow to solve NP-complete problems efficiently. Later, negative results proved the existence of specifically designed hard instances where adiabatic optimization requires exponential time. In spite of this, there was still hope that this would not happen for random instances of NP-complete problems. This is an important issue since random instances are a good model for hard instances that can not be solved by current classical solvers, for which an efficient quantum algorithm would therefore be desirable. Here, we will show that because of a phenomenon similar to Anderson localization, an exponentially small eigenvalue gap appears in the spectrum of the adiabatic Hamiltonian for large random instances, very close to the end of the algorithm. This implies that unfortunately, adiabatic quantum optimization also fails for these instances by getting stuck in a local minimum, unless the computation is exponentially long.

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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