量子算法熵

P. Gács
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引用次数: 83

摘要

将算法信息理论扩展到量子力学,以通用半可计算密度矩阵(“通用概率”)为起点,并将复杂度(算子)定义为其负对数。Kolmogorov复杂度的一些性质自然地扩展到新的领域。近似地说,如果一个量子态在距离低柯尔莫哥洛夫复杂度的低维子空间很小的距离内,它就是简单的。可计算密度矩阵的冯-诺伊曼熵在平均复杂度的加性常数范围内。一些关于随机性的理论也适用于新的领域。我们探索了新量与P.M.B. Vita/spl acute/nyi(1999)定义的量子Kolmogorov复杂度(我们表明后者有时大到2n-2 log n)和A. Berthiaume等人(2000)定义的量子比特复杂度之间的关系。我们的复杂性度量的“克隆”特性与量子比特的复杂性相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum algorithmic entropy
Extends algorithmic information theory to quantum mechanics, taking a universal semi-computable density matrix ("universal probability") as a starting point, and defines complexity (an operator) as its negative logarithm. A number of properties of Kolmogorov complexity extend naturally to the new domain. Approximately, a quantum state is simple if it is within a small distance from a low-dimensional subspace of low Kolmogorov complexity. The von-Neumann entropy of a computable density matrix is within an additive constant from the average complexity. Some of the theory of randomness translates to the new domain. We explore the relations of the new quantity to the quantum Kolmogorov complexity defined by P.M.B. Vita/spl acute/nyi (1999) (we show that the latter is sometimes as large as 2n-2 log n) and the qubit complexity defined by A. Berthiaume et al. (2000). The "cloning" properties of our complexity measure are similar to those of qubit complexity.
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