arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1406.0430quant-ph
收藏

量子因果模型的图分离定理

A graph-separation theorem for quantum causal models

Jacques Pienaar, Caslav Brukner

摘要:

因果模型是物理系统作为有向无环图(DAG)的抽象表示,其中统计依赖性使用称为“d-分离”的图形标准进行编码。Wood和Spekkens最近的工作表明,因果模型通常不能提供量子系统的忠实表示。由于d-分离编码的形式Reichenbach的共同原因原则(RCCP),其有效性是值得怀疑的量子力学,我们提出了一个广义的图形分离规则,不承担RCCP。我们证明了新规则忠实地捕捉到量子网络中的可观测量之间的统计依赖关系,编码为DAG,并且与经典极限中的d-分离一致。我们注意到,由此产生的模型仍然无法给出比量子力学更强的相关性的忠实表示,例如Popescu-Rorlich盒。

英文摘要:

A causal model is an abstract representation of a physical system as a directed acyclic graph (DAG), where the statistical dependencies are encoded using a graphical criterion called `d-separation'. Recent work by Wood & Spekkens shows that causal models cannot, in general, provide a faithful representation of quantum systems. Since d-separation encodes a form of Reichenbach's Common Cause Principle (RCCP), whose validity is questionable in quantum mechanics, we propose a generalised graph separation rule that does not assume the RCCP. We prove that the new rule faithfully captures the statistical dependencies between observables in a quantum network, encoded as a DAG, and is consistent with d-separation in a classical limit. We note that the resulting model is still unable to give a faithful representation of correlations stronger than quantum mechanics, such as the Popescu-Rorlich box.

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。

↑