We introduce a quantum noise diffusion mapping method based on chaotic recurrent neural network in quantum noise cipher system. Simulation results demonstrate the improved security performance in terms of increasing quantum noise masking number.
A ciphertext mapping method based on Gray code is proposed to improve the security of quantum noise stream cipher (QNSC). The experiment is conducted in 1024 × 1024 -quadrature amplitude modulation (QAM) coherent optical communication system over 300 km. Experimental results verify that the proposed scheme does not affect the transmission performance. Meanwhile, the performance analysis reveals that the attacker's bit error rate (BER) increases to about 0.5 per bit position per symbol. The advantage of proposed scheme is that QNSC makes use of basis with fewer bits for encryption/decryption.
ABSTRACT Quantum image encryption is crucial for data protection, but current methods lack attack resistance and have complex encryption processes. This paper proposes a quantum long short ‐ term memory (QLSTM)‐based quantum image encryption method to enhance chaotic sequences and achieve chaos synchronisation control. The QLSTM network improves the Lorenz chaotic sequence, increasing its unpredictability. An adaptive synchronisation control algorithm, using the enhanced chaotic sequence from QLSTM, ensures sender‐receiver synchronization. Optimised through deep neural networks, the system maintains stable synchronization under interference. New cryptographic quantum infrastructure (NCQI) was constructed, and images were encrypted using third‐order radial diffusion, quantum generalised Arnold transform, and quantum W transform. The QLSTM‐improved chaotic sequence showed excellent LLE and 0–1 test results. Information entropy was near 8, with R, G and B channels exceeding 7.999. Anti‐attack analysis revealed high information entropy, strong attack resistance, and number of pixels change rate/unified average changing intensity (NPCR/UACI) values of 99.698% and 33.460%, respectively, indicating significant pixel‐level changes. Combining quantum chaotic system prediction with the QLSTM model enhanced quantum communication stability and anti‐interference ability. This QLSTM‐based quantum encryption method, with chaos synchronisation control, significantly improves encryption security and reliability, maintaining high information entropy and complexity under attacks, proving its effectiveness in image encryption.
In recent years, the transmission security of image information has become an important research direction in the internet field. In this work, we propose a quantum image chaos encryption scheme based on quantum long-short term memory (QLSTM) network. We find that because the QLSTM network has a complex structure and more parameters, when the QLSTM network is used to improve the Lorenz chaotic sequence, its largest Lyapunov exponent is 2.5465% higher than that of the original sequence and 0.2844% higher than that the sequence improved by the classical long-short term memory (LSTM) network, while its result is closer to 1 and more stable in the 0–1 test. The improved sequence of QLSTM network has better chaotic performance and is predicted more difficultly, which improves the security of single chaotic system encryption. The original image is stored in the form of quantum states by using the NCQI quantum image representation model, and the improved sequence of QLSTM network is used to control the three-level radial diffusion, quantum generalized Arnold transform and quantum W-transform respectively, so that the gray value and pixel position of the quantum image are changed and the final encrypted image is obtained. The encryption scheme proposed in this work obtains the average information entropy of all three channels of RGB of greater than 7.999, the average value of pixel number change rate of 99.6047%, the average value of uniform average change intensity of 33.4613%, the average correlation of 0.0038, etc. In the test of statistical properties, the encryption scheme has higher security than some other traditional methods and can resist the common attacks.
Excess noise in continuous-variable quantum key distribution systems usually results in a loss of key rate, leading to fatal security breaches. This paper proposes a long short-term memory time-sequence neural network to predict the key rate of the system while counteracting the effects of excess noise. The proposed network model, which can be updated with historical data, predicts the key rate of the future moment for the input time-sequence data. To increase the key rate, we perform a postselection operation to combat excess noise. We demonstrate the asymptotic security of the protocol against collective attacks with the numerical simulations using the quadrature phase-shift keying protocol, where some parameters have been optimized to resist excess noise. It provides a potential solution for improving the security of quantum communication in practical applications.
Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively large reservoirs by leveraging the unique capabilities of quantum computers, potentially allowing for more efficient and higher performing time-series prediction algorithms. The proposed QESN can be implemented on any digital quantum computer implementing a universal gate set, and does not require any sort of stopping or re-initialization of the circuit, allowing continuous evolution of the quantum state over long time horizons. We conducted simulated QC experiments on the chaotic Lorenz system, both with noisy and noiseless models, to demonstrate the circuit's performance and its potential for execution on noisy intermediate-scale quantum (NISQ) computers.
Images are important data carriers because, compared to text data, they are harder to transfer or store securely and include higher volumes and redundancies of digital data. Images can be shielded against a variety of risks with security, including eavesdropping and illegal copying and alteration. Because of the potential quantum risk to the existing cryptographic encryption methods and the quick advancement towards the development of quantum computers, quantum image encryption algorithms have recently drawn increasing amounts of attention. The majority of quantum image encryption techniques such as diffusion and scrambling, involve two separate rounds. In this model, the three different chaotic maps are used separately for scrambling the images to determine the performance of the quantum image cryptography with different combination of the model. At first, the hash256 algorithm is used for generating the quantum key and the forward diffusion takes place for diffusing the first pixel to final pixel of the input image information. Then, the three different chaotic maps such as pixel permutation, Chen attractor and Lorenz attractor are used for scrambling the input image. Finally, the bit-level permutation and backward diffusion process are considered for the scrambled image. For evaluating the performance of the quantum image cryptography based on the three different chaotic maps, the NPCR, UACI, Entropy, SSIM, correlation characteristics and histogram analysis are determined. The attained NPCR, UACI, Entropy and SSIM of the CASIA2 dataset for Lorenz attractor are improved than the pixel permutation and Chen attractor. Thus, from the attained values, the Quantum Image Cryptography Based on Continuous Chaotic Map such as Lorenz attractor performs better for the statistical and differential analysis than the other chaotic maps.