Welcome to the resource topic for 2023/004
Title:
Quantum Artificial Intelligence on Cryptanalysis
Authors: Hyunji Kim, Sejin Lim, Aubhab Baksi, Dukyoung Kim, Seyoung Yoon, Kyungbae Jang, Hwajeong Seo
Abstract:With the recent development of quantum computers, various studies on quantum artificial intelligence technology are being conducted. Quantum artificial intelligence can improve performance in terms of accuracy and memory usage compared to deep learning on classical computers. In this work, we proposed an attack technique that recovers keys by learning patterns in cryptographic algorithms by applying quantum artificial intelligence to cryptanalysis. Cryptanalysis was performed in the current practically usable quantum computer environment, and this is the world’s first study to the best of our knowledge.
As a result, we reduced 70 epochs and reduced the parameters by 19.6%. In addition, higher average BAP (Bit Accuracy Probability) was achieved despite using fewer epochs and parameters. For the same epoch, the method using a quantum neural network achieved a 2.8% higher BAP with fewer parameters.
In our approach, quantum advantages in accuracy and memory usage were obtained with quantum neural networks. It is expected that the cryptanalysis proposed in this work will be better utilized if a larger-scale stable quantum computer is developed in the future.
ePrint: https://eprint.iacr.org/2023/004
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