9 results about "Conjugate transpose" patented technology

A communication method includes a first device that determines channel state information based on a first frame that is used for channel measurement and that is sent by a second device, performs conjugate transpose and QR decomposition to obtain a matrix R and a matrix Q, and sends, to the second device, the matrix Q indicating attribute information of a target path.

The application belongs to the technical field of multi-channel signal detection, and particularly relates to an adaptive subspace detection method for extended targets, which comprises the following steps: S1: constructing a target signal subspace matrix and an interference signal subspace matrix, which are respectively represented by matrices A and J, and the dimensions of the matrices are N*p and N*q respectively, wherein (p+q)≤N, and N represents the number of system channels; S2: constructing a sample covariance matrix S=YY H using training samples, wherein Y represents the training samples, and the symbol (·) H represents a conjugate transpose. The application integrally realizes interference suppression, signal accumulation and extended target detection by constructing a detection statistic t.

A computing device including one or more processing devices configured to receive classical shadow measurement results associated with a fermion wavefunction. The fermion wavefunction may describe a particle-number-conserving fermion system. The one or more processing devices may compute an estimated k-reduced density matrix (k-RDM), where k is a number of ladder operator pairs. Computing the estimated k-RDM may include computing, as terms of the estimated k-RDM, traces of respective products of a number operator of the particle-number-conserving fermion system, a single-particle basis rotation matrix dependent upon the classical shadow measurement result, elementary symmetric polynomials of Majorana bivectors, and a conjugate transpose of the single-particle basis rotation matrix. Computing each trace may include iteratively computing derivatives of a Pfaffian. The one or more processing devices may output the estimated k-RDM.

The application discloses a quantum state preparation method and a related device, and the method comprises the following steps: determining each partial component of a target quantum state to be prepared, each partial component comprising one ground state of the target quantum state and an amplitude of the ground state; constructing a plurality of target matrices based on the amplitudes of the partial components, and performing SVD decomposition on the target matrices to obtain a unitary matrix, determining a disentanglement quantum logic gate based on the unitary matrix, and applying a plurality of disentanglement quantum logic gates on a quantum bit with an initial state being the target quantum state to obtain a disentanglement quantum circuit; inversely deriving the target quantum circuit from the disentanglement quantum circuit, wherein the target quantum circuit has a target quantum logic gate, and the target quantum logic gate is determined based on a conjugate transpose matrix of the unitary matrix. (logn) The application discloses a quantum state preparation method and a related device, and the method comprises the following steps: determining each partial component of a target quantum state to be prepared, each partial component comprising one ground state of the target quantum state and an amplitude of the ground state; constructing a plurality of target matrices based on the amplitudes of the partial components, and performing SVD decomposition on the target matrices to obtain a unitary matrix, determining a disentanglement quantum logic gate based on the unitary matrix, and applying a plurality of disentanglement quantum logic gates on a quantum bit with an initial state being the target quantum state to obtain a disentanglement quantum circuit; inversely deriving the target quantum circuit from the disentanglement quantum circuit, wherein the target quantum circuit has a target quantum logic gate, and the target quantum logic gate is determined based on a conjugate transpose matrix of the unitary matrix. (logn) The application discloses a quantum state preparation method and a related device, and the method comprises the following steps: determining each partial component of a target quantum state to be prepared, each partial component comprising one ground state of the target quantum state and an amplitude of the ground state; constructing a plurality of target matrices based on the amplitudes of the partial components, and performing SVD decomposition on the target matrices to obtain a unitary matrix, determining a disentanglement quantum logic gate based on the unitary matrix, and applying a plurality of disentanglement quantum logic gates on a quantum bit with an initial state being the target quantum state to obtain a disentanglement quantum circuit

This invention discloses a multi-core computing method and apparatus for QR decomposition that integrates column-block TSQR and tile update. The method includes: for a large-scale matrix to be decomposed, horizontally dividing it into blocks based on the number of cores in the multi-core processor, and further dividing the large-scale matrix into multiple layers according to the tile update method; constructing a multi-stage pipeline with a binary tree structure, performing parallel triangular decomposition on the initial layers of the large-scale matrix through the multi-core processor to obtain the corresponding upper triangular matrix and orthogonal matrix; replacing the original layer block matrix with the upper triangular matrix, performing conjugate transpose on the orthogonal matrix to obtain the update matrix, and implementing parallel updates for the remaining layers through the multi-core processor; based on the reflection vector implicitly stored by the multi-core processor during the parallel triangular decomposition process, solving for the overall orthogonal matrix corresponding to the large-scale matrix, and simultaneously outputting the overall upper triangular matrix obtained after layer update. This invention can realize QR decomposition of large-scale matrices.

A computing device including one or more processing devices configured to receive classical shadow measurement results associated with a fermion wavefunction. The fermion wavefunction may describe a particle-number-conserving fermion system. The one or more processing devices may compute an estimated k-reduced density matrix (k-RDM), where k is a number of ladder operator pairs. Computing the estimated k-RDM may include computing, as terms of the estimated k-RDM, traces of respective products of a number operator of the particle-number-conserving fermion system, a single-particle basis rotation matrix dependent upon the classical shadow measurement result, elementary symmetric polynomials of Majorana bivectors, and a conjugate transpose of the single-particle basis rotation matrix. Computing each trace may include iteratively computing derivatives of a Pfaffian. The one or more processing devices may output the estimated k-RDM.

The invention relates to a GNSS (Global Navigation Satellite System) suppressing interference number identification method, device and equipment and a medium, and relates to the technical field of satellite navigation interference resistance. The method comprises the following steps: receiving a spatial domain signal through an array antenna, obtaining an array baseband signal through down-conversion and analog-to-digital conversion, constructing a spatial domain correlation matrix in a complex matrix form based on signal vectors at a plurality of snapshot moments and conjugate transpose operation, extracting all elements of an upper triangular part of the matrix, arranging the elements according to a preset sequence to form a complex vector as input data, and outputting the input data. And outputting a classification result by using a pre-trained deep learning model, and determining the number of suppressing disturbances. According to the method, data simplification, complete information reservation and adaptive model input are realized by means of complex matrix characteristics, subjective threshold is not needed through deep learning, the limitation of a traditional method is solved, high robustness and accuracy are still achieved under the environment of low signal to interference plus noise ratio and small snapshot number, offline training and real-time reasoning are achieved, the calculation complexity is reduced, the real-time requirement of a terminal is met, and the method is suitable for popularization and application. And reliable support is provided for an anti-interference algorithm.

The invention discloses a matrix inversion method, a matrix inversion circuit, a signal processing method, a signal processing device, an integrated circuit, an electromagnetic wave sensor and terminal equipment, which can realize El Mitt matrix inversion. The El Matrix inversion circuit is used for performing inversion on an El Matrix A. The El Matrix inversion circuit comprises a Cholesky decomposition circuit used for calculating a lower triangular matrix L to enable LLH to be equal to A, and LH is conjugate transpose of L; and the triangular matrix inversion circuit is used for calculating the inverse L-1 of the lower triangular matrix L, and the product of the conjugate transpose of the L-1 and the L-1 is the inverse of the El matrix A.

A computing device including one or more processing devices configured to receive classical shadow measurement results associated with a fermion wavefunction. The fermion wavefunction may describe a particle-number-conserving fermion system. The one or more processing devices may compute an estimated k-reduced density matrix (k-RDM), where k is a number of ladder operator pairs. Computing the estimated k-RDM may include computing, as terms of the estimated k-RDM, traces of respective products of a number operator of the particle-number-conserving fermion system, a single-particle basis rotation matrix dependent upon the classical shadow measurement result, elementary symmetric polynomials of Majorana bivectors, and a conjugate transpose of the single-particle basis rotation matrix. Computing each trace may include iteratively computing derivatives of a Pfaffian. The one or more processing devices may output the estimated k-RDM.