The max-flow min-cut theorem is a cornerstone result in combinatorial optimization. Calegari et al. (arXiv:0802.3208) initialized the study of quantum max-flow min-cut conjecture, which connects the rank of a tensor network and the min-cut. Cui et al. (arXiv:1508.04644) showed that this conjecture is false generally. In this paper, we establish a quantum max-flow min-cut theorem for a new definition of quantum maximum flow. In particular, we show that for any quantum tensor network, there are infinitely many $n$, such that quantum max-flow equals quantum min-cut, after attaching dimension $n$ maximally entangled state to each edge as ancilla. Our result implies that the ratio of the quantum max-flow to the quantum min-cut converges to $1$ as the dimension $n$ tends to infinity. As a direct application, we prove the validity of the asymptotical version of the open problem about the quantum max-flow and the min-cut, proposed in Cui et al. (arXiv:1508.04644 ).
翻译:最大流的细小点数是组合优化的基石 。 Calegari 等人 (arXiv: 08002.3208) 开始研究量最大流小点数的预测, 将强力网络的级别和小点数联系起来 。 Cui 等人 (arXiv: 150.8/ 4644) 显示, 这种推测一般是错误的 。 在本文中, 我们为量最大流的新定义建立一个量最大流微点点数的理论。 特别是, 我们显示, 对于任何量子振幅网络来说, 有无限多的美元, 量最大流等于量计分裁, 在将维度的美元与每个边端以最大串联起来。 我们的结果表明, 量最大流与量小点数的比将达到1美元, 作为维度的美元趋向不精确度。 作为直接应用, 我们证明了关于量子流和量流的开放问题版本的有效性 。 (Cui et al- caliv, 建议的 Civ.