Application of machine learning methods to optimize quantum algorithms in cryptographic analysis tasks

Abstract

In recent years, quantum computing has attracted considerable attention due to its potential to solve complex problems inaccessible to classical computers. One of the priority areas of application of quantum algorithms is cryptographic analysis, since quantum computers are able to effectively crack widely used cryptosystems such as RSA and ECC. However, the development of quantum algorithms faces a number of difficulties related to optimizing their structure and parameters. This study examines the application of machine learning (ML) methods to improve the efficiency of quantum algorithms in cryptographic analysis tasks. To achieve this goal, hybrid quantum-classical approaches were used, combining the advantages of quantum computing and machine learning. Learning algorithms with and without a teacher were used to automatically adjust the parameters of quantum algorithms, including noise reduction and optimization parameters. The research was based on simulation of quantum operations on simulators and testing on real quantum processors. The use of machine learning methods made it possible to reduce the execution time of quantum algorithms by 20-30% while maintaining the accuracy of the result. In addition, learning optimization has reduced the need for quantum resources, such as the number of gates, which opens up opportunities for performing complex tasks on quantum devices with limited power. The use of such approaches has been demonstrated by the example of cryptographic analysis of the Shor algorithm. The results obtained show the effectiveness of integrating machine learning methods into the process of developing quantum algorithms. This opens up prospects for further research aimed at creating more powerful tools for cryptanalysis and other tasks. The study proves the feasibility of using machine learning to optimize quantum algorithms, improving their performance and expanding the scope of possible applications, including cryptographic analysis.