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Products of truncated unitary matrices, independently and uniformly drawn from the unitary group, can be used to study universal aspects of monitored quantum circuits. The von Neumann entropy of the corresponding density matrix decreases with increasing the length L of the product chain, in a way that depends on the matrix dimension N and the truncation depth δN. Here we study that dependence in the double-scaling limit L,N→∞ at the fixed ratio τ=LδN/N. The entropy reduction crosses over from a linear to a logarithmic dependence on τ when this parameter crosses unity. The central technical result is an expression for the singular-value moments of the matrix product in terms of the Erlang function from queueing theory.
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http://dx.doi.org/10.1103/PhysRevE.111.064108 | DOI Listing |
Phys Rev E
June 2025
Universiteit Leiden, Instituut-Lorentz, P.O. Box 9506, 2300 RA Leiden, The Netherlands.
Products of truncated unitary matrices, independently and uniformly drawn from the unitary group, can be used to study universal aspects of monitored quantum circuits. The von Neumann entropy of the corresponding density matrix decreases with increasing the length L of the product chain, in a way that depends on the matrix dimension N and the truncation depth δN. Here we study that dependence in the double-scaling limit L,N→∞ at the fixed ratio τ=LδN/N.
View Article and Find Full Text PDFFront Artif Intell
August 2024
Research Laboratory SIME, ENSIT, University of Tunis, Tunis, Tunisia.
This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models.
View Article and Find Full Text PDFSci Rep
August 2024
Jadara University Research Center, Jadara University, Irbid, Jordan.
Sci Rep
February 2024
College of Weapons, Naval University of Engineering, Wuhan, 430033, Hubei, China.
In order to simultaneously maintain the ship magnetic field modeling accuracy, reduce the number of coefficient matrix conditions and the model computational complexity, an improved composite model is designed by introducing the magnetic dipole array model with a single-axis magnetic moment on the basis of the hybrid ellipsoid and magnetic dipole array model. First, the improved composite model of the ship's magnetic field is established based on the magnetic dipole array model with 3-axis magnetic moment, the magnetic dipole array model with only x-axis magnetic moment, and the ellipsoid model. Secondly, the set of equations for calculating the magnetic moments of the composite model is established, and for the problem of solving the pathological set of equations, the least-squares estimation, stepwise regression method, Tikhonov, and truncated singular value decomposition regularization methods are introduced in terms of the magnetic field, and generalized cross-validation is used to solve the optimal regularization parameters.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2022
This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoising methods make use of decomposition, thresholding and filtering techniques. In the proposed approach, EEG signals are first transformed to orthogonal domain using Tchebichef moments before feeding to the proposed architecture.
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