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The model of superposition queries in symmetric cryptanalysis has yielded remarkable results, such as Simon's period-finding algorithm, which can break many constructions in polynomial time. However, due to limitations of current physical devices, quantum circuits with long depths are often noisy and difficult to realize in practice. The novel computing architecture of distributed quantum computing is expected to reduce the noise and depth of quantum circuits. In this paper, we propose an offline algorithm model that combines distributed Simon's and Grover's algorithms. This model enables us to perform key recovery attacks on different rounds Feistel structures, Even Mansour construction, and the FX construction, while minimizing the quantum query complexity. Despite being limited to classical queries and offline quantum computations, we leverage the algebraic structure of cryptosystems to achieve successful key recovery attacks. [ABSTRACT FROM AUTHOR]

The development of quantum computers has urged the cryptographic community to prepare cryptographic primitives for the eventual arrival of the post-quantum world. At Asiacrypt 2017, Leander and May combined Grover's and Simon's quantum algorithms to break the FX-based block ciphers. Technically this result is based on the combination of the quantum algorithms of Grover's and Simon's for the first time in the cryptographic setting. In this study, we using Bernstein–Vazirani's and Grover's algorithms to generate a new quantum key-recovery attacks on different rounds of Feistel constructions. An advantage of our attack is the keys can be divided into multiple blocks to enter the S-box and realize the process of recovery; this method greatly reduces the complexity of key recovery. Hence, it has strong practicability for key recovery (e.g., DES, Camellia, etc.). In order to show that, a detailed process has been provided in this paper. [ABSTRACT FROM AUTHOR]

Abstract: Statistical analysis is a useful method for setting the whole string of dam displacement measures in mathematical expressions. A statistical model is usually obtained by the method of stepwise regression. The stepwise regressions based on the least square method have limitations such as the lack of stability of the set of selected variables and bias in the parameter estimates. However, the Artificial Immune Algorithm (AIA) provides good performance as an optimization algorithm. This paper proposes an immune statistical model, which merges the statistical model and the immune algorithm together, to resolve the data analysis problems of dam horizontal crest upstream–downstream displacement. The stepwise regression model and immune statistical model have been compared, showing that the immune statistical model provide a higher degree of accuracy in predicting the future behavior of the dam. [Copyright &y& Elsevier]