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经典非因果模型的计算驯服性。

Computational tameness of classical non-causal models.

作者信息

Baumeler Ämin, Wolf Stefan

机构信息

Faculty of Informatics, Università della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland.

Facoltà indipendente di Gandria, Lunga scala, 6978 Gandria, Switzerland.

出版信息

Proc Math Phys Eng Sci. 2018 Jan;474(2209):20170698. doi: 10.1098/rspa.2017.0698. Epub 2018 Jan 10.

Abstract

We show that the computational power of the non-causal circuit model, i.e. the circuit model where the assumption of a is replaced by the assumption of , is completely characterized by the complexity class UP∩coUP. An example of a problem in that class is factorization. Our result implies that classical deterministic closed timelike curves (CTCs) cannot efficiently solve problems that lie of that class. Thus, in stark contrast to other CTC models, these CTCs efficiently solve NP-complete problems, unless NP=UP∩coUP=coNP, which lets their existence in nature appear . This result gives a new characterization of UP∩coUP in terms of fixed points.

摘要

我们证明,非因果电路模型(即把关于a的假设替换为关于b的假设的电路模型)的计算能力完全由复杂度类UP∩coUP来刻画。该类中的一个问题示例是因式分解。我们的结果意味着,经典确定性闭合类时曲线(CTCs)无法有效地解决该类之外的问题。因此,与其他CTCs模型形成鲜明对比的是,除非NP = UP∩coUP = coNP,否则这些CTCs无法有效地解决NP完全问题,这使得它们在自然界中的存在显得可疑。该结果从不动点的角度给出了UP∩coUP的一种新刻画。

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