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 cryptanalyt-ic techniques are essential for developing robust systems that can withstand quantum at-tacks, ensuring encrypted data remains secure and accessible only to authorized parties. This paper introduces the Quantum Hopfield Neural Network (QHopNN) as a novel ap-proach to enhance key recovery in symmetric ciphers. This research provides valuable in-sights 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 op-timization capabilities, QHopNN achieves superior accuracy and efficiency in deciphering encrypted data. Additionally, integrating unitary quantum evolution with dissipative dy-namics further enhances the cryptographic robustness and efficiency of QHopNN. The pro-posed framework is rigorously evaluated using prominent symmetric ciphers, including S-AES and S-DES, and benchmarked against existing state-of-the-art techniques. Experi-mental 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, signifi-cantly outperforming current methods. This breakthrough opens new avenues for advancing cryptanalysis and sets the stage for pioneering future research in quantum-inspired crypto-graphic techniques.