Two optimization methods for quantum simulator implemented on Sunway supercomputer were proposed aiming at the problems of gradual scaling of quantum hardware and insufficient classical simulation speed. Firstly， the tensor contraction operator library SWTT was reconstructed by improving the tensor transposition strategy and computation strategy， which improved the computing kernel efficiency of partial tensor contraction and reduced redundant memory access. Secondly， the balance between complexity and efficiency of path computation was achieved by the contraction path adjustment method based on data locality optimization. Test results show that the improvement method of operator library can improve the simulation efficiency of the "Sycamore" quantum supremacy circuit by 5.4% and the single-step tensor contraction efficiency by up to 49.7 times； the path adjustment method can improve the floating-point efficiency by about 4 times with the path computational complexity inflated by a factor of 2. The two optimization methods have the efficiencies of single-precision and mixed-precision floating-point operations for the simulation of Google’s 53-bit， 20-layer quantum chip random circuit with a million amplitude sampling improved from 3.98% and 1.69% to 18.48% and 7.42% respectively， and reduce the theoretical estimated simulation time from 470 s to 226 s for single-precision and 304 s to 134 s for mixed-precision， verifying that the two methods significantly improve the quantum computational simulation speed.

Pulse condition recognition is one of the important ways of traditional Chinese medical diagnosis. For a long time， recognizing pulse condition based on personal experience restricts the promotion and development of traditional Chinese medicine. Therefore， the researches on using sensing devices for recognizing pulse condition are more and more. In order to solve the problems such as large training datasets， “black box” processing and high time cost in the research of recognizing pulse condition by neural network， a new pulse condition diagram analysis method using Markov decision and Monte Carlo search on the framework of reinforcement learning was proposed. Firstly， based on the theory of traditional Chinese medicine， the paths of specific pulse conditions were classified， and then the representative features for different paths were selected on this basis. Finally， the pulse condition recognition was realized by comparing the threshold values of the representative features. Experimental results show that， the proposed method can reduce the training time and the required resources， retain the complete experience track， and can solve the “black box” problem during the data processing with the accuracy of pulse condition recognition improved.

In recent years, multiscale pedestrian detection received extensive attentions in the field of computer vision. In traditional methods, the input image must be resized with different scales to compute the features, which significantly reduces the detection speed. Color Self-Similarity Feature (CSSF) was presented to overcome this problem. An improved CSSF with lower dimension was proposed for the CSSF whose dimension is too high and time-consuming in the training process of the classifiers. Combined with pedestrian structural similarity, a fixed-size window was defined at first, and then the improved CSSF was extracted by sliding the fixed-size window in different color space. Finally, the pedestrian detection classifier was constructed by combining with AdaBoost algorithm. Test shows that compared with the traditional CSSF whose dimension is ten millions, new feature dimension is only a few thousand, and it can be extracted and trained faster, but detection effect decreases slightly; compared with the Histogram of Oriented Gradient (HOG), feature extraction speed improves 5 times, detection effect is essentially the same. The new method has a good application value in real-time pedestrian detection and monitoring systems.

GPU-based volume rendering has become an active research area in the domain of volume visualization. Large volume data cannot be uploaded directly due to the limitation of GPU memory, which has been a bottleneck of the application of GPU. Bricking method could not only solve this problem, but also maintain the quality of original volume-rendered image. However, the data exchange via the graphics bus is really time consuming and will definitely degrade the render performance. As for these difficulties, the optimal bricking for large volume data was calculated by establishing the model for optimal bricking, and also a 3D texture named node code texture was constructed and distance template was improved to accelerate the octree-based bricking volume rendering. The experimental results illustrate that the proposed method can significantly accelerate the bricking-based volume rendering for large volume data.

The time synchronization mechanism is one of the key problems on Agent-based distributed simulation research. The time synchronization mechanism of Agent-based distributed simulation environment (ADSimE) — conservative mechanism and optimistic mechanism were introduced. That is, the conservative mechanism was used within the node of the platform，while optimistic mechanism was used between the nodes. This kind of time synchronization mechanism could promote the performance of the time advancing in the simulation platform. The principles of the two mechanisms were also introduced. Finally, the key process of the design and running for conservative simulation Agent and optimistic simulation Agent were described in detail.