80 results about "Matrix representation" patented technology

The invention provides a quantum circuit optimization method, device and equipment for a ZUC algorithm subcomponent, and the method comprises the steps: encoding a signal transmitted by communication into quantum bits, and inputting the quantum bits into a ZUC algorithm structure for quantum implementation; representing a first linear subcomponent in the ZUC algorithm structure as a matrix in a binary field; after a matrix in the binary field is expressed as a block matrix, a low-dimensional matrix M in the binary field is derived from the block matrix, and quantum implementation of the M is searched by using a search algorithm designed based on a matrix decomposition principle; and according to a quantum gate parallel rule, carrying out optimized multi-layer division on the quantum of the M again, obtaining the quantum of the M, carrying out optimized layering operation, and optimizing the quantum of the first linear subcomponent. According to the method provided by the invention, the quantum circuit of the subcomponent of the ZUC algorithm is optimized, so that the resource overhead of quantum implementation of the ZUC algorithm is saved.

The invention relates to the technical field of textile production scheduling and resource allocation planning, in particular to a textile equipment scheduling management and optimization system of a textile factory, which comprises a data perception and integration module, a scheduling optimization decision module, a plan execution and equipment control module and a closed-loop feedback and self-learning module. The data sensing module collects and fuses order data, equipment operation state data and production environment data in real time, and a unified real-time data view is generated through cleaning and alignment processing; the scheduling module runs a mixed integer programming dynamic model, and minimizes the comprehensive cost and synchronously optimizes the equipment utilization rate and energy consumption in combination with rolling horizon optimization under the condition of meeting the process constraints of order delivery time limit and process dependency matrix representation; the plan execution module analyzes the scheduling instruction into an equipment executable instruction, drives equipment operation and collects execution deviation; and the closed loop module triggers rescheduling when the deviation exceeds the limit or the order is plugged. The scheduling accuracy and adaptability are improved, the cost is reduced, and efficient and stable production is guaranteed.

The invention discloses a multi-modal knowledge graph representation method and device, and belongs to the field of computer vision representation. The method comprises the following steps: firstly, acquiring multi-modal embedding of a target entity, including a visual modal, a text modal and a structural modal, and calculating a Gram matrix formed by the multi-modal embedding; and calculating a determinant of the Gram matrix, and minimizing a geometric volume represented by the Gram matrix to coordinate semantic alignment among different modals and optimize multi-modal representation. A volume minimization constraint is introduced through a GraCon contrast learning framework, the geometric volume of multi-modal embedding is further optimized, and semantic consistency between different modals is ensured. Entity anchoring matching loss is introduced, and each entity is aligned by using a structural mode, so that the accuracy and consistency of entity representation are remarkably enhanced. And a Transform encoder is used to carry out reasoning and prediction of a knowledge graph completion task. The multi-modal representation quality can be improved, the entity representation is optimized, and the knowledge graph completion precision and robustness are improved.

Techniques described herein provide for automated detection of near-duplicate documents. In one example, a system can cluster documents into a set of clusters based on character frequencies associated with the documents. For a given cluster, the system can generate first similarity scores associated with every pair of documents in the cluster. The system can then select a filtered group of documents associated with first similarity scores that meet or exceed a first predefined similarity threshold. Next, the system can convert the filtered group of documents into matrix representations. The system can generate second similarity scores for every pair of matrix representations. The system can then identify documents, from among the filtered group of documents, associated with second similarity scores that meet or exceed a second predefined similarity threshold. The identified documents can be duplicate or near-duplicate text documents.

The invention provides a decision tree security reasoning method and framework based on an efficient element selection protocol, and belongs to the technical field of data security processing. According to the method, nodes and user attributes of a decision tree are converted into two-dimensional matrix representation from one-dimensional vectors, and a constant round communication conversion protocol is designed based on function secret sharing, so that a ciphertext selection index is converted into two ciphertext one-hot vectors; and performing ciphertext dot product operation on the two-dimensional matrix and the two one-hot vectors to complete an element security selection function. After a one-dimensional vector is converted into a two-dimensional matrix, through ciphertext dot product operation, the communication size is not directly proportional to the number of nodes and the number of attributes any more and is only directly proportional to the square root of the number of elements. According to the method, a secure dot product and function secret sharing technology based on secret sharing is combined, the communication size is reduced to be in direct proportion to a square root from being in direct proportion to the number of nodes and the number of attributes, communication and calculation expenses are remarkably reduced, and an efficient privacy protection decision tree reasoning framework is provided.

The invention provides an artificial intelligence model accelerated training reasoning method and device, and belongs to the field of artificial intelligence, and the method comprises the steps: firstly carrying out the grouping disassembly of a large-dimension matrix in an artificial intelligence model through employing a lamination grouping technology, and converting the large-dimension matrix into a lamination block matrix structure; then, carrying out compression decomposition on the non-diagonal block matrix by utilizing an ACA-SVD matrix decomposition technology, and representing that two small matrixes are multiplied; then diagonal matrix blocks of the obtained laminated block matrix structure are extracted in sequence, the matrix is expressed as multiplication of a plurality of block diagonal matrixes, and the block diagonal matrixes are stored respectively; when model training and reasoning are carried out, a matrix multiplication process can be converted into multiplication of a plurality of block diagonal matrixes, a matrix inversion process can be converted into inversion of the plurality of block diagonal matrixes, the block diagonal matrixes are mutually independent, a recursive relationship does not exist in an operation process, and highly parallel operation can be realized.

The invention discloses a knowledge graph embedding and link prediction optimization method based on matrix representation and Grassmann manifold optimization, and the method comprises the steps: making a high-quality matrix representation template as input, generating an embedded matrix through a plurality of models, carrying out the coding through a Grassmann manifold optimization algorithm, and generating a multi-dimensional space vector code. According to the method, adaptive iterative detection of information correction, semantic precision and deviation control and a similarity algorithm based on rule template matching are further combined, iterative correction optimization is carried out on model output, and comprehensive judgment is carried out on the model output and a gold label, so that the explanation accuracy and quality are improved. According to the method, the problems of information normalization, semantic precision deviation and rule consistency of an existing model can be effectively solved, the overall performance of knowledge graph embedding and link prediction is improved, output of different models is kept in a balanced state, and the defects of a single model are overcome.

The invention discloses a base station positioning method and system, and relates to the technical field of indoor positioning, and the method comprises the steps: obtaining target scene space structure information and signal propagation shielding characteristics which meet preset space constraints, building a scene shielding model, determining a candidate deployment region, and eliminating a high-attenuation shielding region. Acquiring a base station position Pi and a distance Lij between a label and the base station in the candidate area, and constructing a label position solving equation containing vector and matrix representation; and determining a base station layout avoidance scheme, deploying base stations, re-collecting data, correcting distance data, substituting the corrected distance data into the equation to obtain a label accurate position by combining the condition that the matrix determinant is close to zero and a scene shielding model, and obtaining a label positioning result. The system comprises a scene modeling module, a candidate area determination module, a data acquisition and equation construction module, a base station layout optimization module, a distance data correction module and a label position solving module, and solves the problems of unreasonable layout and large distance measurement error of the existing method.

A computer-implemented method includes: accessing ground-force signal traces measured at each source location when applying a pilot sweep signal characterized by a sweeping instantaneous frequency that includes a fundamental component, wherein each ground-force signal trace comprises a fundamental mode signal component as well as at least one harmonic mode signal component; obtaining an analytic function representation for the pilot sweep signal and each ground-force signal trace, wherein the analytic function representation comprises a phase portion; establishing a matrix representation characteristic of the phase portion of the analytic function representation for the fundamental component in the ground-force signal, and wherein the matrix representation is size limited; retrieving, from the analytic function representation for each ground-force signal trace and after inverting the matrix representation, the fundamental mode signal component of the ground-force signal trace; and providing the retrieved fundamental mode signal component of each ground-force signal trace for subsequent data processing.

The invention relates to a data commodity recommendation method and system based on deep learning, and the method comprises the steps: carrying out the word embedding coding of a user text and a commodity text of a data commodity through a first deep learning model, and obtaining a user text matrix representation and a commodity text matrix representation; respectively carrying out feature extraction and feature compression on the user text matrix representation and the commodity text matrix representation by utilizing a second deep learning model to obtain a user text vector representation and a commodity text vector representation; fusing the user text vector representation and the commodity text vector representation to obtain a fusion vector; and calculating a prediction score of the data commodity according to the fusion vector. According to the method, the data commodity recommendation effect can be improved, the recommended data commodity can meet the purchase demand of the user, and the user experience and the transaction efficiency are improved.

The invention relates to the technical field of multi-modal model reasoning, in particular to a visual language interpretable learning method and system assisted by neural logic reasoning. The method comprises the following steps: carrying out vector representation on an image-text pair; performing relation matrix representation on the first-order logic; performing logic combination and multi-hop reasoning based on the first-order logic and the representation of the image-text pair; based on logic combination and multi-hop reasoning, a logic reasoning micro attention network fused with a visual language model is constructed. According to the method, the LogicVLM model is designed, so that a microframework combining neural inductive learning and logical reasoning can be realized. And further learning first-order logic and logic combination from the input visual text semantic concept, constructing a path of a tree structure to execute multi-hop reasoning, and completing a complex visual language reasoning task. In an experiment, compared with a traditional visual language model, the model training speed and the reasoning speed of the method are basically kept unchanged, and various tasks are remarkably improved.

The invention relates to the technical field of network security data monitoring, in particular to a network security evaluation system for a network security server based on data monitoring, which comprises a traffic spectrum analysis module, a time sequence generation module, a data processing module and a data processing module, the method comprises the following steps: constructing a spectrogram of network traffic time slice data, sorting according to the magnitude through frequency amplitudes, and analyzing frequently appearing behavior characteristics to obtain initial abnormal traffic characteristics; according to the method, the frequency characteristics of the traffic time sequence are mined through spectral analysis, the spectrogram is constructed through Fourier transform operation, and the high-frequency abnormal nodes are screened out, so that the perception capability of potential threats is enhanced. Through extraction of the feature state sequence and matrix representation of the transition probability of the feature state sequence, the system can update the flow state in real time in a dynamic flow environment, an abnormal change point in the camouflage flow is sensitively positioned, and accurate identification of the camouflage flow is realized.

The invention relates to a gradient perturbation optimization method for implicit matrix decomposition based on differential privacy, and belongs to the field of computer recommendation systems and the field of information security. An implicit feedback matrix composed of 0 and 1 is used for representing feedback data of a user to an item, 0 represents negative feedback, and 1 represents positive feedback. Randomly initializing a user implicit factor matrix W and a project implicit factor matrix H; a data set and a target function L (W, H) are constructed, W and H are optimized and solved by adopting a matrix decomposition method based on Bayesian personalized sorting, and a result W is stored. Randomly initializing a noise adding item implicit factor matrix # imgabs0 # and an initial record matrix # imgabs1 # by using the trained W, substituting the W into L (W, H), constructing a target function # imgabs3 # about # imgabs2 #, calculating a gradient # imgabs6 # of # imgabs4 # about # imgabs5 #, cutting the gradient by using a cutting threshold C, and obtaining an optimal privacy budget size lambda t in each round of training by using an overall mse calculation formula, then, the gradient is disturbed by using lambda t and the sensitivity of the gradient to obtain # imgabs7, the # imgabs8 is aggregated to obtain a cumulative gradient, and # imgabs9 is used for updating an item implicit factor matrix by using the cumulative gradient and # imgabs10; # imgabs11 is repeatedly updated to obtain a final item implicit factor matrix # imgabs12, and the final item implicit factor matrix # imgabs12 is distributed to each user; and the user u calculates a predicted value # imgabs13 on the equipment of the user u according to the predicted value, sorts unobserved interaction items in a descending order, and finally selects the first K items from the interaction items to recommend to the user u. The method has good privacy protection and model accuracy.

The invention discloses a coding and decoding method for LDPC codes under time and frequency selective channels in an NAVDAT system, and the method comprises the steps: determining a basis matrix and an expansion factor Z according to an information bit length and a target code length of an input bit sequence before coding, and the Z is a positive integer; obtaining a low density parity check (LDPC) matrix based on the basis matrix and the expansion factor Z; the method comprises the following steps: encoding an input bit sequence by using a low density parity check (LDPC) matrix under an NAVDAT system time and frequency selective channel, wherein the code rate is 3 / 4 or 1 / 2; wherein a basis matrix of the LDPC matrix comprises a sub-matrix A and a sub-matrix B, if the basis matrix is expressed as an m-row n-column matrix, the sub-matrix A is an m-row n-m-column matrix, the sub-matrix B is an m-row m-column matrix, and the sub-matrix B comprises a double-diagonal structure matrix B1, a single-diagonal structure matrix B2 and a column B3 with the weight of 4; and outputting the coded or decoded bit sequence. According to the invention, the coding requirements of information bit sequences with various lengths can be met.

The invention discloses a seismic source positioning method considering the influence of an abnormal value and a dynamic wave velocity, and the method comprises the steps: arranging sensors to detect sound waves emitted by a seismic source, and observing the time when the sound waves reach each sensor; subtracting the control equation established based on the reference sensor from the control equation established based on the measurement sensor, and carrying out linearization and matrix representation on a nonlinear equation line obtained by subtraction; the matrix is solved, an analytic solution enabling the sum of squares of the residual errors to be minimum is found, the obtained analytic solution is substituted into an overdetermined matrix equation to obtain a fitting model, and weight estimation is conducted according to the relation between the residual errors and weights; iteration is carried out between weight estimation and overdetermined matrix solving, after optimal weight estimation is obtained, overdetermined matrix solving is completed according to the optimal weight, and a final seismic source positioning result is obtained based on an analytical solution. According to the method, the technical problem that the influence of abnormal values and wave velocity measurement errors on the seismic source positioning precision in a complex engineering environment is large is solved, and the fault tolerance and the real-time performance of seismic source positioning are improved.

The invention discloses a community discovery method of a graph neural network based on multi-view information fusion, and belongs to the technical field of data mining. The method comprises the following steps: preprocessing data, performing PCA dimension reduction on an attribute matrix, and normalizing an adjacent matrix; inputting the correlation matrix combination into an automatic encoder and a graph attention automatic encoder to obtain a new matrix representation and fusing the new matrix representation into a final node representation matrix; then back-propagating the optimization model by using various loss functions; and finally, community division is realized by using a k-means clustering algorithm. According to the method, the structure and attribute information of the original data are fully utilized, multi-view information is effectively fused, the accuracy of community discovery is improved, and the method has great significance in the fields of research on citation networks, recommendation systems and the like.

Provided is a dynamic time warping (DTW)-based real-time diagnosis method for a commutation failure (CF). The method includes: designing a time series template S0 containing a change of a firing angle of a phase-controlled rectifier, and having a length n and a Euclidean distance L; using an n*n matrix M to represent a Euclidean distance between each point in the S0 and each point in the Si, and constricting the matrix M with an Itakura window; calculating values of the matrix M in the search range H; setting different weights for data at different locations, and searching a shortest path L* from point (x1, y1) to point (xn, yn) of the matrix; and setting a range of the distance L of the S0 as λ, determining that the CF fault occurs if the L* is greater than λL, or otherwise, determining that a system works normally.

The application provides a decoding method, an encoding method, a device, a chip and a computing device. The method comprises the following steps: a decoding device acquires a first matrix, and then decodes a first code word vector according to the first matrix to obtain a decoding result. The first matrix is a 0-1 matrix representing elements in a second matrix on a polynomial ring. The second matrix is a check matrix on a finite field 2q, and the number of rows is r and the number of columns is n. A first polynomial corresponding to the polynomial ring is a q-th irreducible polynomial on a binary field. The number of 1s in the first matrix is less than the number of 1s in the 0-1 matrix representation of the second matrix. The first code word vector comprises k information code elements and r check code elements, and n=k+r. Each code element comprises q data units, and each data unit comprises at least one bit. The scheme can effectively reduce the hardware implementation complexity and cost of RS encoding and decoding.

The invention relates to the technical field of kinetic analysis of mechanical structures, in particular to a structural modal parameter identification method based on joint low-rank tensor decomposition, which comprises the following steps: performing tensor modeling on expression X = phi Q of structural response under modal coordinates, performing segmentation operation on collected vibration response, and expressing the vibration response in a high-order tensor form; then, low-rank representation is carried out on each column of modal shape vectors and low-rank representation is carried out on each row of modal responses by utilizing the low-rank performance of the modal response vectors and the modal shape vectors; further, the combined low-rank tensor of the high-order tensor is decomposed and solved, and a matrix representation phi reflecting a modal shape and a matrix representation Q of modal response are obtained; and finally, extracting modal frequency and damping ratio information from the obtained Q. According to the structural modal parameter identification method, the dynamic parameters such as the structural modal frequency and the modal shape are effectively identified by using the low-rank characteristics of the modal response vector and the modal shape vector.

The application provides a power load anomaly detection method and device, and belongs to the technical field of power grid safety. The method comprises the following steps: standardizing power load data and processing the power load data in blocks. Then, a multi-head self-attention network is used to calculate an attention matrix representation between blocks and within blocks, and the attention matrix representation is respectively up-sampled. Next, a divergence loss function of the two is calculated, and an anomaly score of each point is calculated according to the divergence loss function. Finally, whether the power load data is abnormal is determined through a preset hyperparameter threshold. Through the method, the distance between normal and abnormal user features is maximized, the distance between features of the same type of users is minimized, an effective power data representation is learned, different user power consumption data features are actively compared, and abnormal power consumption behaviors are effectively identified.

Certain examples of the present disclosure relate to an apparatus (10, 120) comprising: means (11, 15) for sending, to one or more User Equipment, UE (110), demodulation reference signal, DMRS, matrix information (401) indicative of a row-orthogonal DMRS matrix (P); means (11, 15) for sending, to a UE (UEi) of the one or more UE, DMRS submatrix information (402) indicative of a submatrix (Pi) of the row-orthogonal DMRS matrix that has been assigned to the UE; means (11, 15) for receiving, from the UE, said at least one DMRS determined by the UE based, at least in part, on the DMRS matrix information and the DMRS submatrix information.

Methods, systems and apparatus for simulating physical systems. In one aspect, a method includes the actions of selecting a first set of basis functions for the simulation, wherein the first set of basis functions comprises an active and a virtual set of orbitals; defining a set of expansion operators for the simulation, wherein expansion operators in the set of expansion operators approximate fermionic excitations in an active space spanned by the active set of orbitals and a virtual space spanned by the virtual set of orbitals; performing multiple quantum computations to determine a matrix representation of a Hamiltonian characterizing the system in a second set of basis functions, computing, using the determined matrix representation of the Hamiltonian, eigenvalues and eigenvectors of the Hamiltonian; and determining properties of the physical system using the computed eigenvalues and eigenvectors.

The invention discloses a layered firepower planning method fusing a diffusion model and a chaos polynomial, and belongs to the field of spacecraft manufacturing and application. The target damage expectation evaluation method including energy field rasterization unified matrix representation, diffusion model energy field prediction and chaotic polynomial damage expectation calculation is established, and the efficiency and convenience of damage effect evaluation are improved. And counting energy field information contained in each grid based on a rasterization preprocessing method of the maximum range of the energy field. A hierarchical optimized firepower planning architecture is established, aiming point planning efficiency is improved, a top layer determines a distribution relation matrix of the aircraft and the target for firepower distribution, meanwhile, the incidental damage effect of the aircraft on the target is considered by utilizing a grouping strategy, and on the basis, a bottom layer aims at each group; and aiming point optimization is carried out by using a rapid and accurate target damage expectation evaluation method, an aiming point planning result is rapidly obtained, and the aircraft efficiency is exerted in high real-time performance according to the aiming point planning result.

The present invention discloses a network embedding method, device, electronic device, and computer program product based on a federated learning network. The method comprises: obtaining local network matrices of multiple data holders in a federated learning network, wherein the data holders are participants in the federated learning network, the federated learning network is pre-divided into multiple local learning networks, and the local network matrix is ​​a matrix representation of the local learning network; encrypting the local network matrix using a preset mask matrix to obtain a local encryption matrix; uploading the local encryption matrices corresponding to the multiple data holders to an embedding server, and receiving a global encryption matrix obtained by integrating the multiple local encryption matrices from the embedding server; and decrypting the global encryption matrix based on the preset mask matrix to obtain a global embedding matrix. The present invention solves the technical problem of poor data security in network embedding in a distributed environment in the prior art.

This invention relates to the field of drawing recognition technology, and discloses a method, apparatus, device, and readable storage medium for beam span recognition in vector graphics. The method includes: acquiring a target vector graphic and its corresponding matrix representation; identifying key beam span points from the matrix representation; determining the beam span type based on these key points; and mapping the positions of the key beam span points to the target vector graphic to obtain beam span elements corresponding to the beam span type in the target vector graphic. By implementing this invention, the recognition of beam span elements is not limited by the drawing method, ensuring the accuracy of beam span element recognition. This enables accurate identification of the positional information of beam elements, further guaranteeing accurate identification and attribute matching of beam elements.

The invention provides a security detection method and device for network traffic data, and the method comprises the steps: extracting a plurality of preset network features of each traffic data packet in a target time period, and obtaining a plurality of pieces of evolution data; dividing the plurality of evolution data of the target time period into to-be-detected data of a plurality of windows by using a sliding window; processing the to-be-detected data of each window to obtain a matrix representation of the to-be-detected data; determining an approximation ratio test value distribution based on the matrix representation of the data to be detected; determining a threshold value based on the approximate ratio test value distribution and a preset false alarm probability; whether the to-be-detected data is larger than a threshold value or not is judged, and if yes, it is determined that the to-be-detected data is abnormal data; and processing the abnormal data by utilizing hash function calculation and bit operation, and judging whether the abnormal data is attack data or not according to a processing result. According to the invention, the detection efficiency and detection precision of the network traffic data are improved.

The invention discloses a coding and decoding method for LDPC codes under a slight fading Gaussian channel in an NAVDAT system, and the method comprises the steps: determining a basis matrix and an expansion factor Z according to an information bit length and a target code length of an input bit sequence before coding, and the Z is a positive integer; obtaining a low density parity check (LDPC) matrix based on the basis matrix and the expansion factor Z; the method comprises the following steps: encoding an input bit sequence by using a low density parity check (LDPC) matrix under a slight fading Gaussian channel of an NAVDAT system, wherein the code rate is 3 / 4 or 1 / 2; wherein a basis matrix of the LDPC matrix comprises a sub-matrix A and a sub-matrix B, if the basis matrix is expressed as an m-row n-column matrix, the sub-matrix A is an m-row n-m-column matrix, the sub-matrix B is an m-row m-column matrix, and the sub-matrix B comprises a double-diagonal structure matrix B1, a single-diagonal structure matrix B2 and a column B3 with the weight of 4; and outputting the coded or decoded bit sequence. According to the invention, the coding requirements of information bit sequences with various lengths can be met.

The invention discloses a neural network platform novel recommendation method based on node feature enhancement, and belongs to the field of deep learning, a heterogeneous graph neural network model based on node feature enhancement is constructed, and the model comprises a small-degree node connectivity enhancement module, a path purification module and an inter-path aggregation module; the method specifically comprises the following steps: collecting all novel historical data of all users from a current novel platform, processing the data into a heterogeneous graph form, and carrying out feature conversion; based on a small-degree node connectivity enhancement module, expanding a connection relationship of small-degree nodes; purifying the element paths based on a path purification module; generating a final embedded matrix representation of the node based on an inter-path aggregation module; constructing a loss function optimization training model; and obtaining novel data of the current user, inputting the trained model, and generating a personalized novel recommendation list according to the preference of each user. According to the invention, the accuracy of novel recommendation is remarkably improved, and the novel which is most matched with the preference is recommended to the user.

The invention discloses a model lightweight method based on space-level low-rank decomposition distillation. The method comprises the following steps: selecting and training a teacher model; performing low-rank decomposition on the weight matrix of the teacher model, and obtaining low-rank matrix representation through a singular value decomposition technology; and taking the teacher model after low-rank decomposition as a student model, and transmitting knowledge of the teacher model before low-rank decomposition to the student model through a distillation technology. According to the method, the low-rank decomposition technology is combined with model distillation, so that the model complexity is reduced and the model performance is maintained at the same time.

The present disclosure provides a quantum circuit simulation method, apparatus, and electronic device, relating to the technical field of quantum computing, specifically to the technical field of quantum circuits. The specific implementation solution is as follows: Obtain the structural information of a quantum circuit, where the quantum circuit includes N quantum operations, and the N quantum operations include quantum noise; based on the structural information, obtain the matrix representation of the superoperator of the quantum operations in the quantum circuit and the qubits acted on by the quantum operations; for each quantum operation, taking the qubits acted on by the quantum operation as the target index order, perform a target operation on the first tensor representation of the first quantum state during the quantum circuit simulation to obtain the matrix representation of the first quantum state, and perform matrix multiplication processing on the matrix representation of the superoperator of the quantum operation and the matrix representation of the first quantum state to obtain the simulation result of the quantum circuit; the target operation includes a shift-axis operation and a matrix reshaping operation, and the first quantum state is obtained through the evolution of the quantum operation starting from the initial quantum state of the quantum circuit.