Quantum engineering 

Abstract: Quantum key distribution (QKD) enables two remote users to share a string of key bits with information-theoretical security. Parameter optimization is a crucial step in achieving optimal performance in practical QKD systems. In general, such optimization is implemented using a local search algorithm (LSA). However, LSAs inevitably fail to find out the optimal values when the searched key rate function is nonconvex or has a discontinuity of first-order derivatives and a narrow parameter search space. This paper proposes a genetic algorithm-based method to overcome the limitations of LSAs for QKD parameter optimization. We tested the proposed method with various types of common QKD protocols and found that it has very high parameter optimization performance for QKD with a time consumption comparable to that using a standard LSA. We expect our method to be a valuable optimization tool for quantum information processing tasks.

Abstract: Quantum information transfer is an information processing technology with high speed and high entanglement with the help of quantum mechanics principles. To solve the problem of quantum information getting easily lost during transmission, we choose topological quantum error correction codes as the best candidate codes to improve the fidelity of quantum information. The stability of topological error correction codes brings great convenience to error correction. The quantum error correction codes represented by surface codes have produced very good effects in the error correction mechanism. In order to solve the problem of strong spatial correlation and optimal decoding of surface codes, we introduced a reinforcement learning decoder that can effectively characterize the spatial correlation of error correction codes. At the same time, we use a double-layer convolutional neural network model in the confrontation network to find a better error correction chain, and the generation network can approach the best correction model, to ensure that the discriminant network corrects more nontrivial errors. To improve the efficiency of error correction, we introduced a double-Q algorithm and ResNet network to increase the error correction success rate and training speed of the surface code. Compared with the previous MWPM 0.005 decoder threshold, the success rate has slightly improved, which can reach up to 0.0068 decoder threshold. By using the residual neural network architecture, we saved one-third of the training time and increased the training accuracy to about 96.6%. Using a better training model, we have successfully increased the decoder threshold from 0.0068 to 0.0085, and the depolarized noise model being used does not require a priori basic noise, so that the error correction efficiency of the entire model has slightly improved. Finally, the fidelity of the quantum information has successfully improved from 0.2423 to 0.7423 by using the error correction protection schemes.

Abstract: Solving for quantum error correction remains one of the key challenges of quantum computing. Traditional decoding methods are limited by computing power and data scale, which restrict the decoding efficiency of color codes. There are many decoding methods that have been suggested to solve this problem. Machine learning is considered one of the most suitable solutions for decoding task of color code. We project the color code onto the surface code, use the deep Q network to iteratively train the decoding process of the color code and obtain the relationship between the inversion error rate and the logical error rate of the trained model and the performance of error correction. Our results show that through unsupervised learning, when iterative training is at least 300 times, a self-trained model can improve the error correction accuracy to 96.5%, and the error correction speed is about 13.8% higher than that of the traditional algorithm. We numerically show that our decoding method can achieve a fast prediction speed after training and a better error correction threshold.

Abstract: In order to boost the security and confidentiality of information in quantum images, on the foundation of the NEQR model, a novel quantum watermarking scheme combining quantum Hilbert scrambling with steganography based on the Moiré fringe is designed in this paper. First of all, for carrier image, and watermark image, the color information and position information are denoted, respectively, by the NEQR model. Next, the watermark image is converted to a disordered image by quantum Hilbert scrambling, and the message of the original watermark image cannot be gained from the disordered image. At last, the watermark image after scrambling is embedded into the carrier image through the steganography of the Moiré fringe, obtaining the watermarked image. Due to the unitary image of the quantum gate, quantum Hilbert inverse scrambling is the opposite process of quantum Hilbert scrambling. In addition, the watermark image can be completely extracted from the watermarked image. What’s more, the experimental simulation and performance analysis of the scheme are done. The experimental simulation proves the feasibility of this algorithm. Visually, there is no difference between the carrier image and the watermarked image. The PSNR between the watermarked image and the carrier image is measured, which quantitatively shows the high similarity. In addition, the time complexity of the quantum circuit is lower than some other quantum image watermarking schemes, which proves the simplicity of this scheme.