Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks.

The emotional support dialogue system is an emerging and challenging task in natural language processing to alleviate people’s emotional distress. Each utterance in the dialogue has features such as emotion, intent, and commonsense knowledge. Previous research has indicated subpar performance in strategy prediction accuracy and response generation quality due to overlooking certain underlying factors. To address these issues, we propose Advanced Multi-Task Learning and Feature-Fusion for Emotional Support Conversation (AdMISC), which extracts various potential factors influencing dialogue through neural networks, thereby improving the accuracy of strategy prediction and the quality of generated responses. Specifically, we extract features affecting dialogue through dynamic emotion extraction and commonsense enhancement and then model strategy prediction. Additionally, the model learns these features through attention networks to generate higher quality responses. Furthermore, we introduce a method for automatically averaging loss function weights to improve the model’s performance. Experimental results using the emotional support conversation dataset ESConv demonstrate that our proposed model outperforms baseline methods in both strategy label prediction accuracy and a range of automatic and human evaluation metrics.