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Quantum Neural Network Based Distinguisher on SPECK-32/64.
Hyunji Kim1, Kyungbae Jang1, Sejin Lim1
1Division of IT Convergence Engineering, Hansung University, Seoul 02876, Republic of Korea.
Sensors (Basel, Switzerland)
|July 8, 2023
Summary
This study introduces the first quantum neural network distinguisher for the SPECK-32 block cipher, demonstrating its potential despite current quantum computing limitations. While not outperforming classical methods, it shows promise for future advancements in quantum cryptography.
Area of Science:
- Quantum Computing and Cryptography
- Machine Learning in Cybersecurity
Background:
- Lightweight block ciphers like SPECK-32 are crucial for securing Internet of Things (IoT) sensor data.
- Deep learning has emerged as a powerful tool for analyzing differential characteristics of block ciphers.
- The advancement of quantum computing necessitates exploring quantum machine learning for cryptographic analysis.
Purpose of the Study:
- To propose and evaluate the first quantum neural network (QNN) based distinguisher for the SPECK-32 block cipher within the Noisy Intermediate-Scale Quantum (NISQ) era.
- To analyze the performance of a QNN distinguisher under constrained quantum computing environments.
- To investigate the impact of various QNN parameters on distinguisher performance.
Main Methods:
- Development of a quantum neural distinguisher tailored for the SPECK-32 block cipher.
- Experimental evaluation of the QNN distinguisher on a NISQ device, assessing its accuracy and operational range (up to 5 rounds).
- Comparative analysis against a classical deep learning distinguisher.
- In-depth analysis of factors influencing QNN performance, including embedding methods, qubit count, and quantum layers.
Main Results:
- The quantum neural distinguisher successfully operated for up to 5 rounds of SPECK-32.
- Achieved an accuracy of 0.53, demonstrating its capability as a distinguisher (accuracy > 0.51) despite limitations.
- Classical neural distinguisher achieved higher accuracy (0.93) due to current quantum hardware constraints.
- Identified key QNN parameters (embedding, qubit count, layers) affecting performance, highlighting the need for careful tuning beyond resource scaling.
Conclusions:
- The proposed quantum neural distinguisher is a pioneering step in applying QNNs to lightweight block cipher cryptanalysis in the NISQ era.
- Current quantum hardware limitations restrict performance compared to classical methods, but the distinguisher proves functional.
- Future improvements in quantum resources and parameter optimization are essential for enhancing QNN-based cryptographic distinguishers.
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