Abstract: A distributed quantum computing simulation method and apparatus. The method includes: converting a quantum circuit to be simulated into a tensor network that is represented by an undirected graph, and segmenting the undirected graph into a plurality of sub-graphs by using a genetic algorithm that is based on operation resources of a distributed system; respectively performing, on sub-process nodes, tensor contraction and merging on the plurality of sub-graphs for connected tensors until only one tensor is left to finally obtain zero-order tensors of the plurality of sub-graphs at the same time; and acquiring and superposing the zero-order tensors of the plurality of sub-graphs from the sub-process nodes at the same time to determine a zero-order tensor of the undirected graph, and using the zero-order tensor of the undirected graph as a probability amplitude of a positive operator value measurement element, so as to perform quantum computing simulation.

Abstract: A video data processing method, including: obtaining three-dimensional feature data and three-dimensional weight data corresponding to video data; separately preprocessing the three-dimensional feature data and the three-dimensional weight data to obtain a feature value matrix and a weight value matrix; and inputting the feature value matrix and the weight value matrix into a plurality of three-dimensional systolic arrays for parallel computing to obtain a video data processing result. The present method can fully expand the degree of parallelism of computation and a four-dimensional systolic computation architecture is constructed by using multiple three-dimensional systolic arrays, so as to perform parallel computing on a three-dimensional feature value matrix and a three-dimensional weight value matrix, thereby shortening the computation time of three-dimensional convolution, and improving the video data processing efficiency.

Abstract: Disclosed is an image processing method. In the invention, weighted calculation is carried out on a first quantification threshold value obtained with a saturated mapping method and a second quantification threshold value obtained with an unsaturated mapping method, that is, two quantification threshold values are fused, and an obtained optimal quantification threshold value can be suitable for most activation output layers, and therefore, effective information of the activation output layers can be more effectively reserved, the optimal quantification threshold value is used for subsequent image processing work, and the precision of reasoning and calculation of a quantized deep neural network on a low-bit-width hardware platform is improved. An image processing apparatus and device are disclosed, which have the same beneficial effects as the image processing method.

Abstract: An image registration method and apparatus, an electronic apparatus, and a storage medium are provided. The image registration method comprises: extracting edge pixels of the binocular image, and determining high-confidence parallax points in the edge pixels and parallax; projecting each non-high-confidence parallax point to the triangular mesh in a direction parallel with a parallax dimension to obtain a triangular face; determining a parallax search range of parallax of each non-high-confidence parallax point, calculating a matching cost corresponding to all parallax in each parallax search range, and determining that parallax with the smallest matching cost is the parallax of the corresponding non-high-confidence parallax point; and determining a depth boundary point in the edge pixels, determining a parallax boundary point of each depth boundary point in a target direction, and setting parallax of pixels between the depth boundary point and the parallax boundary point to a target value.

Abstract: Provided are a method and apparatus for measuring the quantum decoherence degree, an electronic device and a computer-readable storage medium. The method includes: determining an initial quantum state and a point to be measured of a quantum system to be measured (S101); amplifying the amplitude of the quantum state of the point to be measured, and measuring a decoherence degree of the point to be measured (S102); and calculating a decoherence coefficient of the quantum system to be measured according to the decoherence degree (S103). According to the method for measuring the quantum decoherence degree, the amplitude of the quantum state of the point to be measured in the quantum system to be measured is amplified, and the probability of the point to be measured is obtained by means of measurement, so as to measure the decoherence degree of the point to be measured.

Abstract: A data storage method, apparatus, and device, and a readable storage medium. The method includes: after a random access memory is powered on, obtaining target data to be stored in a fixed storage address of the random access memory; determining a target transmission mode from a bit value change transmission mode and a bit value fixed transmission mode, wherein the target transmission mode is different from a historical transmission mode determined after the random access memory is powered on last time; and transmitting the target data from and to the random access memory according to the target transmission mode. The method can prevent data from being stolen after power-down of the target data, and guarantees the data security.

Abstract: A quantum data erasure method, system and device, and a readable storage medium. The method includes: acquiring an equal-probability quantum state system; measuring the equal-probability quantum state system to collapse the equal-probability quantum state system into a binary random number sequence; generating a corresponding random angle value according to the binary random number sequence; and performing a bitwise rotation operation on quantum data in a quantum device according to the random angle value to complete this quantum data erasure. In the present application, the introduction of a quantum true random number can ensure that erased data will not be recovered and reversely cracked, and is of great value in protecting data assets; moreover, randomly processed data still has the characteristics such as quantum coherence and quantum entanglement, and can be used in subsequent operations, whereby a time-consuming labor-intensive process of preparing a quantum system is not required every time.

Abstract: Provided are an image data storage method, an image data processing method and system, and a related apparatus. The image data processing method includes the following steps: sequentially storing image data in a dynamic random memory according to a preset storage format, so that adjacent pieces of image data in the dynamic random memory have continuous storage addresses; reading a preset number of pieces of multi-channel parallel image data from the dynamic random memory, and storing the multi-channel parallel image data in a first-in first-out memory of an FPGA; and executing a convolution operation on target image data in the first-in first-out memory to obtain image feature data. By means of the method, the image data processing rate can be increased.

Abstract: A system for accelerating an RNN network including: a first cache, which is used for outputting Wx1 to WxN or Wh1 to WhN in parallel in N paths in a cyclic switching manner, and the degree of parallelism is k; a second cache, which is used for outputting xt or ht-1 in the cyclic switching manner; a vector multiplication circuit, which is used for, by using N groups of multiplication arrays, respectively calculating Wx1xt to WxNxt, or respectively calculating Wh1ht-1 to WhNht-1; an addition circuit, which is used for calculating Wx1xt+Wh1ht-1+b1 to WxNxt+WhNht-1+bN; an activation circuit, which is used for performing an activation operation according to an output of the addition circuit; a state updating circuit, which is used for acquiring ct-1, calculating ct and ht, updating ct-1, and sending ht to the second cache; a bias data cache; a vector cache; and a cell state cache.

Abstract: A method includes: setting, in a main training network, an auxiliary training network having the same architecture as the main training network, performing data enhancement on an original image to obtain an enhanced image; inputting the original image into the main training network, inputting the enhanced image into the auxiliary training network; determining whether an intersection-over-union value of a second prediction frame generated by the auxiliary training network and a target frame is greater than an intersection-over-union value of a first prediction frame generated by the main training network and the target frame; and in response to the intersection-over-union value of the second prediction frame and the target frame being greater than the intersection-over-union value of the first prediction frame and the target frame, replacing the intersection-over-union value of the first prediction frame and the target frame with the intersection-over-union value of the second prediction frame and the target f

Abstract: Provided are a content-adaptive binocular matching method and apparatus. The method includes: dividing both left and right images into grids so as to calculate feature information at grid points, and performing matching calculation between the left and right images at the grid points according to the feature information so as to determine points with high matching degrees as reference points; calculating parallax values of the reference points, determining an interlayer spacing, and layering all the reference points in a depth direction according to the parallax values and the interlayer spacing; for a non-reference point pixel in a certain image, determining a reference point closest to the non-reference point pixel, and determining a search range and the parallax value of the reference point; and performing matching calculation on the non-reference point pixel and a pixel in the search range in the other image.

Abstract: A data storage method, apparatus, and device, and a readable storage medium. The method includes: after a random access memory is powered on, obtaining target data to be stored in a fixed storage address of the random access memory; determining a target transmission mode from a bit value change transmission mode and a bit value fixed transmission mode, wherein the target transmission mode is different from a historical transmission mode determined after the random access memory is powered on last time; and transmitting the target data from and to the random access memory according to the target transmission mode. The method can prevent data from being stolen after power-down of the target data, and guarantees the data security.

Abstract: Provided are a turbulence field update method, apparatus, and device, and a computer-readable storage medium. The method includes: obtaining sample turbulence data; performing model training by use of the sample turbulence data to obtain a reinforcement learning turbulence model; calculating initial turbulence data of a turbulence field by use of a Reynolds Averaged Navior-Stokes (RANS) equation; processing the initial turbulence data by use of the reinforcement learning turbulence model to obtain a predicted Reynolds stress; and performing calculation on the predicted Reynolds stress by use of the RANS equation to obtain updated turbulence data.

Abstract: Disclosed is an image segmentation method, including: obtaining an original image set; performing feature extraction on the original image set by using a backbone network to obtain a feature map set; performing channel extraction fusion processing on the feature map set by using a channel extraction fusion model to obtain an enhanced feature map set; and segmenting the enhanced feature map set by using a preset convolutional neural network to obtain an image segmentation result. In addition, the present application also provides an image segmentation system and device, and a readable storage medium, which have the beneficial effects above.

Abstract: Provided are a content-adaptive binocular matching method and apparatus. The method includes: dividing both left and right images into grids so as to calculate feature information at grid points, and performing matching calculation between the left and right images at the grid points according to the feature information so as to determine points with high matching degrees as reference points; calculating parallax values of the reference points, determining an interlayer spacing, and layering all the reference points in a depth direction according to the parallax values and the interlayer spacing; for a non-reference point pixel in a certain image, determining a reference point closest to the non-reference point pixel, and determining a search range and the parallax value of the reference point; and performing matching calculation on the non-reference point pixel and a pixel in the search range in the other image.

Abstract: A hardware environment-based data quantization method includes: parsing a model file under a current deep learning framework to obtain intermediate computational graph data and weight data that are independent of a hardware environment; performing calculation on image data in an input data set through a process indicated by an intermediate computational graph to obtain feature map data; separately performing uniform quantization on the weight data and the feature map data of each layer according to a preset linear quantization method, and calculating a weight quantization factor and a feature map quantization factor (S103); combining the weight quantization factor and the feature map quantization factor to obtain a quantization parameter that makes hardware use shift instead of division; and finally, writing the quantization parameter and the quantized weight data to a bin file according to a hardware requirement so as to generate quantized file data (S105).

Abstract: Provided in the present invention are an image segmentation method and apparatus.

Abstract: The present invention provides a method and apparatus for data caching. The method comprises: output matrixes are acquired one by one, a plurality of acquired output matrixes are written alternately into two queue sets of a first cache unit according to a sequence in which the output matrixes are acquired, and the output matrixes stored line by line in a first cache unit are written into a second cache unit one by one, according to the sequence in which the output matrixes are written into the second cache unit, valid data of each output matrix of the second cache unit is determined one by one according to preset parameters, and the valid data of each output matrix is written into a third cache unit, and the valid data of the output matrixes stored in the third cache unit are configured to be sequentially written into a memory according to a sequence in which the valid data are written into the third cache unit.

Abstract: A high-resolution image matching method and system are provided. The method includes performing regional fidelity down-sampling on an initial high-resolution image to obtain a multi-level low-resolution image, performing local matching on the obtained multi-level low-resolution images using a method with global probes to obtain a matching result of the low-resolution images, and performing reverse refinement on the obtained matching result of the low-resolution image using overall consistency of the image matching, to obtain the matching results of the high-resolution images at all levels until the matching results of the initial resolution images are obtained, so as to reduce the computational complexity of the image matching process and improve the accuracy of the matching result, and then the matching result of the high-resolution image is obtained through reverse refinement based on the overall consistency.