To address the limitations of the traditional SM4 key expansion scheme in terms of resistance to quantum computing threats and the lack of independence among round keys, this paper presents an enhanced key expansion scheme based on sponge construction. We proposed an iterative function based on Feistel structure for the absorbing and squeezing stages, alongside a substitution-permutation (SP) function designed for the mixing stage. In statistical tests, the average P-value rose by 9.09%. In individual tests, the P-value increased by up to 79.2%. Moreover, in avalanche effect tests, our first round key flipped 16.383, 15.901, and 15.906 bits on average, compared with 9.109 bits for SM4. Our smaller variances in the number of flipped bits also indicate better stability. These results shows that our design enhancing the security and flexibility of SM4.

With the increasing scale of data and complexity of problems, some researchers have explored the integration of parameterized quantum circuits (PQCs) within convolutional neural networks (CNNs) as a means to enhance algorithmic performance. However, in most current quantum convolutional neural networks (QCNN) models, a single topological structure for quantum kernels (qkernels) is adopted and only one qubit of qkernels is measured, both of which may limit the model’s performance. To solve these problems, a novel multi-topology quantum convolutional neural networks with qubit-measurement attention for image classification is proposed. In order to enhance the capability of feature extraction, a multi-topology PQCs strategy is proposed, i.e., we adopt the different topology PQCs to construct quantum convolutional layers. In addition, a qubit-measurement attention mechanism is designed to mitigate the significant loss of entanglement information during the measurement phase. Specifically, each qubit in the qkernel is measured to generate a local feature map, and the weight of each local feature map is then calculated, resulting in the final feature map. Nine image classification experiments conducted on CIFAR-10 demonstrate that our model outperforms the state-of-the-art QCNN model, achieving an improvement of 11.7% on ten categories classification. Our model not only introduces a new approach for constructing QCNNs but also provides valuable reference for designing attention mechanisms tailored to quantum computing.

Precise measurement of carotid artery blood flow is of vital importance for studying thrombosis and early carotid atherosclerotic plaque. However, the traditional non-parametric methods are limited by the weak detection ability to low-velocity blood flow, and show problems including the large measurement deviation and long algorithm running time. Motivated by the above status quo, a novel method based on deep complex convolutional neural network (DCCNN) is proposed for carotid blood flow velocimetry. Based on supervised learning, DCCNN feeds the echo signals into complex convolutional layers for the purpose of rejecting clutter signals. Then, the outputs of complex convolutional layers are processed by the complex fully connected layers to estimate the blood flow velocity. The effectiveness of the proposed method is verified by simulation as well as in vivo data of healthy volunteers. Compared with typical velocimetry methods such as the high-pass filter and singular value decomposition, the normalized root mean square error (NRMSE) of the velocimetry result obtained from the proposed method is reduced by 47.20%) and 45.45%, and the goodness-of-fit is improved by 5.64%, 3.36%, respectively. In addition, the running time of DCCNN is reduced by 82.10% and 21.11%, respectively. Such results show that the proposed method is a promising tool for blood flow velocity measurement due to its higher velocity measurement accuracy and good real-time performance.