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.

The pay-per-use licensing of the Intellectual Property （IP） core enables the system designer to purchase IP at low price according to the actual situation， and has become a major method of IP licensing. To meet the pay-per-use demand of IP core， based on Reconfigurable Finite State Machine （RFSM） and Physical Unclonable Function （PUF）， a new IP licensing scheme RFSM-PUF was proposed for Field Programmable Gate Array （FPGA） IP. Aiming at the problem that the protocols of the IP protection schemes of different manufacturers cannot be used universally， an IP protection authentication protocol for the proposed scheme was proposed to ensure the confidentiality and flexibility of IP authentication. Firstly， RFSM was embedded in the Original Finite State Machine （OFSM） in the IP， and in this way， the IP was only unlocked by the IP core designer. Then， the challenges were input into the PUF circuit to produce responses. Finally， the cipher consisting of the license and PUF responses was input into the RFSM to unlock the IP. The security analysis results show that the proposed scheme meets various security indicators. RFSM-PUF scheme was tested on the LGSyth91 benchmark circuits. Experimental results show that on the premise of meeting various safety indicators， the proposed scheme reduces 1 377 Look-Up Tables （LUT） averagely at every IP core compared to the PUF based pay-per-use licensing scheme， so that the hardware overhead is significantly reduced.

Most of the existing multi-view clustering algorithms assume that there is a linear relationship between multi-view data points， and fail to maintain the locality of original feature space during the learning process. At the same time， merging subspace in Euclidean space is too rigid to align learned subspace representations. To solve the above problems， a multi-view clustering algorithm via subspaces merging on Grassmann manifold was proposed. Firstly， the kernel trick and the learning of local manifold structure were combined to obtain the subspace representations of different views. Then， the subspace representations were merged on the Grassmann manifold to obtain the consensus affinity matrix. Finally， spectral clustering was performed on the consensus affinity matrix to obtain the final clustering result. And Alternating Direction Method of Multipliers （ADMM） was used to optimize the proposed model. Compared with Kernel Multi-view Low-Rank Sparse Subspace Clustering （KMLRSSC） algorithm， the proposed algorithm has the clustering accuracy improved by 20.83 percentage points， 9.47 percentage points and 7.33 percentage points on MSRCV1， Prokaryotic and Not-Hill datasets. Experimental results verify the effectiveness and good performance of the multi-view clustering algorithm via subspace merging on Grassmann manifold.

Since some computation in reachability Query Preserving Graph Compression （QPGC） algorithm are redundant， a high-performance compression strategy was proposed. In the stage of solving the vertex sets of ancestors and descendants， an algorithm named TSB （Topological Sorting Based algorithm for solving ancestor and descendant sets） was proposed for common graph data. Firstly， the vertices of the graph data were topological sorted. Then， the vertex sets were solved in the order or backward order of the topological sequence， avoiding the redundant computation caused by the ambiguous solution order. And an algorithm based on graph aggregation operation was proposed for graph data with short longest path， namely AGGB （AGGregation Based algorithm for solving ancestor and descendant sets）， so the vertex sets were able to be solved in a certain number of aggregation operations. In the stage of solving reachability equivalence class， a Piecewise Statistical Pruning （PSP） algorithm was proposed. Firstly， piecewise statistics of ancestors and descendants sets were obtained and then the statistics were compared to achieve the coarse matching， and some unnecessary fine matches were pruned off. Experimental results show that compared with QPGC algorithm： in the stage of solving the vertex sets of ancestors and descendants， TSB and AGGB algorithm have the performance averagely increased by 94.22% and 90.00% respectively on different datasets； and in the stage of solving the reachability equivalence class， PSP algorithm has the performance increased by more than 70% on most datasets. With the increasing of the dataset， using TSB and AGGB cooperated with PSP has the performance improved by nearly 28 times. Theoretical analysis and simulation results show that the proposed strategy has less redundant computation and faster compression speed compared to QPGC.

The conventional Deep Belief Network (DBN) often utilizes the method of randomly initializing the weights and bias of Restricted Boltzmann Machine(RBM) to initialize the network. Although it could overcome the problems of local optimality and long training time to some extent, it is still difficult to further achieve higher accuracy and better learning efficiency owing to the huge difference between reconstruction and original input resulting from random initialization. In view of the above-mentioned problem, a kind of DBN model based on Sparse Denoising AutoEncoder (SDAE) was proposed. The advantage of the advocated model was the feature extraction by SDAE. Firstly, SDAE was trained, and then, the obtained weights and bias were utilized to initialize DBN. Finally, DBN was trained. Experiments were performed on card game data set of Poker hand and handwriting data sets of MNIST and USPS to verify the performance of the proposed model. In Poker hand data set, compared with the conventional DBN, the error rate of the proposed model is lowered by 46.4%, the accuracy rate and the recall rate are improved by 15.56% and 14.12% respectively. The results exhibit that the proposed method is superior to other existing methods in recognition performance.

To overcome the salient extraction results cannot preserve edge and enrich the inner details when extracting image salient region, a new multi-scale extraction approach based on frequency domain was proposed. In order to remove redundant information and get the innovation, the image was Fourier-transformed to get the spectral residual on multiple resolutions. Then normalization processing was applied to obtain the final saliency image. The simulation results show that the proposed method has good visual effect, which can keep the edges of salient region and highlight the whole significant target uniformly at the same time. The area under Receiver Operating Characteristic (ROC) curve of these results also has satisfied performance.

Multi-view stereo image composition is largely dependent on the type of multi-view stereo display device. Currently, optical-plate-based multi-view stereo LCD display is most popular, while there is lack of a general composition method for this kind of display. A new general composition method was proposed for the most popular optical-plate-based multi-view stereo LCD display. The method is made up of three parts, i.e. sub-pixel judgment, sub-pixel sub-sampling for each view, and sub-pixel arrangement and composition of each view. This method covers all the possibilities of optical-plate-based multi-view stereo LCD display, with good applicability and popularity. The correctness and validity of the proposed method is verified by experiments.

Based on MPEG-4 FGS technology, a new scalable coding algorithm for multi-view stereo video was proposed, combining FGS scalability with views scalability. In the proposed algorithm, the middle-view sequence was coded as a base layer and a FGS enhancement layer, with other view sequences all predicted from the middle-view sequence yielding each view's FGS enhancement layers. In this coding structure, three cases (I, P, B frames) were considered, and the corresponding improvements had been implemented. The new method can provide a flexible scalability performance, and it can match many different kinds of transmission requirements. Experimental results proved the multi-view scalability performance of the proposed scheme.