Paper 2022/1671

Quantum Neural Network based Distinguisher for Differential Cryptanalysis on Simplified Block Ciphers

Hyunji Kim, Hansung University
Kyungbae Jang, Hansung University
Sejin Lim, Hansung University
Yeajun Kang, Hansung University
Wonwoong Kim, Hansung University
Hwajeong Seo, Hansung University

Differential cryptanalysis is a block cipher analysis technology that infers a key by using the difference characteristics. Input differences can be distinguished using a good difference characteristic, and this distinguishing task can lead to key recovery. Artificial neural networks are a good solution for distinguishing tasks. For this reason, recently, neural distinguishers have been actively studied. We propose a distinguisher based on a quantum-classical hybrid neural network by utilizing the recently developed quantum neural network. To our knowledge, we are the first attempt to apply quantum neural networks for neural distinguisher. The target ciphers are simplified ciphers (S-DES, S-AES, S-PRESENT-[4]), and a quantum neural distinguisher that classifies the input difference from random data was constructed using the Pennylane library. Finally, we obtained quantum advantages in this work: improved accuracy and reduced number of parameters. Therefore, our work can be used as a quantum neural distinguisher with high reliability for simplified ciphers.

Available format(s)
Attacks and cryptanalysis
Publication info
Quantum Neural Network Simplified Block Ciphers Distinguisher
Contact author(s)
khj1594012 @ gmail com
starj1023 @ gmail com
dlatpwls834 @ gmail com
etus1211 @ gmail com
dnjsdndeee @ gmail com
hwajeong84 @ gmail com
2022-12-02: approved
2022-12-01: received
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      author = {Hyunji Kim and Kyungbae Jang and Sejin Lim and Yeajun Kang and Wonwoong Kim and Hwajeong Seo},
      title = {Quantum Neural Network based Distinguisher for Differential Cryptanalysis on Simplified Block Ciphers},
      howpublished = {Cryptology ePrint Archive, Paper 2022/1671},
      year = {2022},
      note = {\url{}},
      url = {}
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