QHopNN: Investigating Quantum Advantage in Cryptanalysis Using a Quantum Hopfield Neural Network

Abstract Cryptanalysis is crucial for securing cryptographic systems, particularly with the advent of quantum computing, which threatens traditional encryption methods. Advanced cryptanalytic techniques are essential for developing robust systems that can withstand quantum attacks, ensuring encrypted data remains secure and accessible only to authorized parties. This paper introduces the Quantum Hopfield Neural Network (QHopNN) as a novel approach to enhance key recovery in symmetric ciphers. This research provides valuable insights into integrating quantum principles with neural network architectures, paving the way for more secure and efficient cryptographic systems. By leveraging quantum principles like superposition and entanglement, along with Hopfield networks’ pattern recognition and optimization capabilities, QHopNN achieves superior accuracy and efficiency in deciphering encrypted data. Additionally, integrating unitary quantum evolution with dissipative dynamics further enhances the cryptographic robustness and efficiency of QHopNN. The proposed framework is rigorously evaluated using prominent symmetric ciphers, including S-AES and S-DES, and benchmarked against existing state-of-the-art techniques. Experimental results compellingly demonstrate the superiority of QHopNN in key recovery, with a mean Bit Accuracy Probability (BAP) of 0.9706 for S-AES and 0.9815 for S-DES, significantly outperforming current methods. This breakthrough opens new avenues for advancing cryptanalysis and sets the stage for pioneering future research in quantum-inspired cryptographic techniques.