论文标题
部分可观测时空混沌系统的无模型预测
Quantum entropy expansion using n-qubit permutation matrices in Galois field
论文作者
论文摘要
随机数对于任何加密应用都至关重要。但是，由于熵剥夺了伪随机数生成器和未加密的物联网，因此流过Internet的数据并不安全。在这项工作中，我们解决了几种数据格式的熵较小的问题。具体而言，我们使用与N Qubit置换矩阵相关的大信息空间来扩展任何数据的熵，而不会增加数据的大小。我们将英文文字带有每个字节范围4-5位的熵。我们使用一组N Qubit（N $ \ leq $ 10）排列矩阵来操纵数据，并观察到操纵数据中熵的扩展（每字节超过7.9位）。我们还观察到其他数据格式（例如图像，音频等）的类似行为（n $ \ leq $ 15）。
Random numbers are critical for any cryptographic application. However, the data that is flowing through the internet is not secure because of entropy deprived pseudo random number generators and unencrypted IoTs. In this work, we address the issue of lesser entropy of several data formats. Specifically, we use the large information space associated with the n-qubit permutation matrices to expand the entropy of any data without increasing the size of the data. We take English text with the entropy in the range 4 - 5 bits per byte. We manipulate the data using a set of n-qubit (n $\leq$ 10) permutation matrices and observe the expansion of the entropy in the manipulated data (to more than 7.9 bits per byte). We also observe similar behaviour with other data formats like image, audio etc. (n $\leq$ 15).
