Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum-classical hybrid architecture. This architecture utilizes a pre-trained multilingual BERT model to obtain vector representations of words and combines the proposed Batched Upload Quantum Recurrent Neural Network (BUQRNN) and Parameter Non-shared Batched Upload Quantum Recurrent Neural Network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.

In the era of noisy intermediate-scale quantum (NISQ) computing, the synergistic collaboration between quantum and classical computing models has emerged as a promising solution for tackling complex computational challenges. Long short-term memory (LSTM), as a popular network for modeling sequential data, has been widely acknowledged for its effectiveness. However, with the increasing demand for data and spatial feature extraction, the training cost of LSTM exhibits exponential growth. In this study, we propose the quantum convolutional long short-term memory (QConvLSTM) model. By ingeniously integrating classical convolutional LSTM (ConvLSTM) networks and quantum variational algorithms, we leverage the variational quantum properties and the accelerating characteristics of quantum states to optimize the model training process. Experimental validation demonstrates that, compared to various LSTM variants, our proposed QConvLSTM model outperforms in terms of performance. Additionally, we adopt a hierarchical tree-like circuit design philosophy to enhance the model’s parallel computing capabilities while reducing dependence on quantum bit counts and circuit depth. Moreover, the inherent noise resilience in variational quantum algorithms makes this model more suitable for spatiotemporal sequence modeling tasks on NISQ devices.

Cardiovascular disease has seriously affected the lives of modern people. One of the most commonly used imaging methods for diagnosing cardiovascular disease is computed tomography angiography (CTA). To generate a diagnosis report for doctors, every coronary artery needs to be identified and segmented, including the right coronary artery (RCA), the posterior descending artery (PDA), the posterior lateral branch (PLB), the left circumflex (LCx), the left anterior descending branch (LAD), the ramus intermedius (RI), the obtuse marginal branches (OM1, OM2), and the diagonal branches (D1, D2). In this paper, we proposed a coronary artery automatic identification algorithm, which performs better in terms of accuracy than other similar algorithms and works efficiently. Normally, each Coronary Computed Tomographic Angiography (CCTA) dataset can be completed within seconds. This algorithm fully complies with the coronary label standard established by the Society of Cardiovascular Computed Tomography (SCCT). This algorithm has been put into operation in more than 100 hospitals for over one year. According to all previous tests, the labels obtained from the algorithm were compared with results manually corrected by several experts. Among 892 CCTA datasets, 95.96% of the labels obtained from the algorithms were correct.

Nowcasting has emerged as a critical foundation for services including heavy rain alerts and public transportation management. Although widely used for short-term forecasting, models such as TrajGRU and PredRNN exhibit limitations in predicting low-intensity rainfall and low temporal resolution, resulting in suboptimal performance during infrequent heavy rainfall events. To tackle these challenges, we introduce a spatio-temporal sequence and generative adversarial network model for short-term precipitation forecasting based on radar data. By enhancing the ConvLSTM model with a pre-trained TransGAN generator, we improve feature resolution. We first assessed the model’s performance on the Moving MNIST dataset and subsequently validated it on the HKO-7 dataset. Employing metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Structural Similarity Index Measure (SSIM), Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR), we compare our model’s performance to existing models. Experimental results reveal that our proposed ConvLSTM-TransGAN model effectively captures weather system evolution and surpasses the performance of other traditional models.

Enhancing the robustness of complex networks is of great practical significance as it ensures the stable operation of infrastructure systems. We measure its robustness by examining the size of the largest connected component of the network after initial attacks. However, traditional research on network robustness enhancement has mainly focused on low-order networks, with little attention given to higher-order networks, particularly higher-low order coupling networks(the largest connected component of the network must exist in both higher-order and low-order networks). To address this issue, this paper proposes robust optimization methods for higher-low order coupled networks based on the greedy algorithm and the simulated annealing algorithm. By comparison, we found that the simulated annealing algorithm performs better. The proposed method optimizes the topology of the low-order network and the higher-order network by randomly reconnecting the edges, thereby enhancing the robustness of the higher-order and low-order coupled network. The experiments were conducted on multiple real networks to evaluate the change in the robustness coefficient before and after network optimization. The results demonstrate that the proposed method can effectively improve the robustness of both low-order and higher-order networks, ultimately enhancing the robustness of higher-low order coupled networks.