The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale ...The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale quantum(NISQ)devices due to their excessive depth and circuit complexity.We demonstrate a high-precision phase difference estimation protocol based on the Bayesian phase difference estimation algorithm and single-photon projective measurement.The iterative framework of the algorithm,combined with the independence from controlled unitary operations,inherently mitigates circuit depth and complexity limitations.Through an experimental realization on the photonic system,we demonstrate high-precision estimation of diverse phase differences,showing root-mean-square errors(RMSE)below the standard quantum limit𝒪(1/√N)and reaching the Heisenberg scaling𝒪(1/N)after a certain number of iterations.Our scheme provides a critical advantage in quantum resource-constrained scenarios,and advances practical implementations of quantum information tasks under realistic hardware constraints.展开更多
The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the m...The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.展开更多
To avoid the complicated motion compensation in interferometric inverse synthetic aperture(InISAR)and achieve realtime three-dimensional(3 D)imaging,a novel approach for 3 D imaging of the target only using a single e...To avoid the complicated motion compensation in interferometric inverse synthetic aperture(InISAR)and achieve realtime three-dimensional(3 D)imaging,a novel approach for 3 D imaging of the target only using a single echo is presented.This method is based on an isolated scatterer model assumption,thus the scatterers in the beam can be extracted individually.The radial range of each scatterer is estimated by the maximal likelihood estimation.Then,the horizontal and vertical wave path difference is derived by using the phase comparison technology for each scatterer,respectively.Finally,by utilizing the relationship among the 3 D coordinates,the radial range,the horizontal and vertical wave path difference,the 3 D image of the target can be reconstructed.The reconstructed image is free from the limitation in InISAR that the image plane depends on the target's own motions and on its relative position with respect to the radar.Furthermore,a phase ambiguity resolution method is adopted to ensure the success of the 3 D imaging when phase ambiguity occurs.It can be noted that the proposed phase ambiguity resolution method only uses one antenna pair and does not require a priori knowledge,whereas the existing phase ambiguity methods may require two or more antenna pairs or a priori knowledge for phase unwarping.To evaluate the performance of the proposed method,the theoretical analyses on estimation accuracy are presented and the simulations in various scenarios are also carried out.展开更多
In this study, a classical spectral-finite difference scheme （SFDS） for the three-dimensional （3D） parabolic equation is reduced by using proper orthogonal decomposition （POD） and singular value decomposition （...In this study, a classical spectral-finite difference scheme （SFDS） for the three-dimensional （3D） parabolic equation is reduced by using proper orthogonal decomposition （POD） and singular value decomposition （SVD）. First, the 3D parabolic equation is discretized in spatial variables by using spectral collocation method and the discrete scheme is transformed into matrix formulation by tensor product. Second~ the classical SFDS is obtained by difference discretization in time-direction. The ensemble of data are comprised with the first few transient solutions of the classical SFDS for the 3D parabolic equation and the POD bases of ensemble of data are generated by using POD technique and SVD. The unknown quantities of the classical SFDS are replaced with the linear combination of POD bases and a reduced- order extrapolation SFDS with lower dimensions and sufficiently high accuracy for the 3D parabolic equation is established. Third, the error estimates between the classical SFDS solutions and the reduced-order extrapolation SFDS solutions and the implementation for solving the reduced-order extrapolation SFDS are provided. Finally, a numerical example shows that the errors of numerical computations are consistent with the theoretical results. Moreover, it is shown that the reduced-order extrapolation SFDS is effective and feasible to find the numerical solutions for the 3D parabolic equation.展开更多
In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust s...In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust sample autocorrelations based on ranks and signsto obtain estimators for the parameters of the Poisson INAR(1) process. The effects ofadditive outliers on the estimates of parameters of integer-valued time series are examined. Some numerical results of the estimators are presented with a discussion of theobtained results. The proposed methods are applied to a dataset concerning the numberof different IP addresses accessing the server of the pages of the Department of Statistics of the University of Würzburg. The results presented here give motivation to use themethodology in practical situations in which Poisson INAR(1) process contains additiveoutliers.展开更多
基金Project supported by the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20233001 and BK20243060)the National Natural Science Foundation of China(Grant No.62288101)。
文摘The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale quantum(NISQ)devices due to their excessive depth and circuit complexity.We demonstrate a high-precision phase difference estimation protocol based on the Bayesian phase difference estimation algorithm and single-photon projective measurement.The iterative framework of the algorithm,combined with the independence from controlled unitary operations,inherently mitigates circuit depth and complexity limitations.Through an experimental realization on the photonic system,we demonstrate high-precision estimation of diverse phase differences,showing root-mean-square errors(RMSE)below the standard quantum limit𝒪(1/√N)and reaching the Heisenberg scaling𝒪(1/N)after a certain number of iterations.Our scheme provides a critical advantage in quantum resource-constrained scenarios,and advances practical implementations of quantum information tasks under realistic hardware constraints.
基金Project(2010ZX03006-004) supported by the National Science and Technology Major Program of ChinaProject(YYYJ-1113) supported by the Knowledge Innovation Program of the Chinese Academy of SciencesProject(2011CB302901) supported by the National Basic Research Program of China
文摘The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.
基金supported by the Science and Technique Commission Foundation of Fujian Province(2018H6023)。
文摘To avoid the complicated motion compensation in interferometric inverse synthetic aperture(InISAR)and achieve realtime three-dimensional(3 D)imaging,a novel approach for 3 D imaging of the target only using a single echo is presented.This method is based on an isolated scatterer model assumption,thus the scatterers in the beam can be extracted individually.The radial range of each scatterer is estimated by the maximal likelihood estimation.Then,the horizontal and vertical wave path difference is derived by using the phase comparison technology for each scatterer,respectively.Finally,by utilizing the relationship among the 3 D coordinates,the radial range,the horizontal and vertical wave path difference,the 3 D image of the target can be reconstructed.The reconstructed image is free from the limitation in InISAR that the image plane depends on the target's own motions and on its relative position with respect to the radar.Furthermore,a phase ambiguity resolution method is adopted to ensure the success of the 3 D imaging when phase ambiguity occurs.It can be noted that the proposed phase ambiguity resolution method only uses one antenna pair and does not require a priori knowledge,whereas the existing phase ambiguity methods may require two or more antenna pairs or a priori knowledge for phase unwarping.To evaluate the performance of the proposed method,the theoretical analyses on estimation accuracy are presented and the simulations in various scenarios are also carried out.
基金This work was supported in part by the National Natural Science Foundation of China （Grant Nos. 11271127, 11361035）, the Doctoral Foundation of Guizhou Normal University, and the Science and Technology Fund of Guizhou Province （Grant No. 7052） in 2014.
文摘In this study, a classical spectral-finite difference scheme （SFDS） for the three-dimensional （3D） parabolic equation is reduced by using proper orthogonal decomposition （POD） and singular value decomposition （SVD）. First, the 3D parabolic equation is discretized in spatial variables by using spectral collocation method and the discrete scheme is transformed into matrix formulation by tensor product. Second~ the classical SFDS is obtained by difference discretization in time-direction. The ensemble of data are comprised with the first few transient solutions of the classical SFDS for the 3D parabolic equation and the POD bases of ensemble of data are generated by using POD technique and SVD. The unknown quantities of the classical SFDS are replaced with the linear combination of POD bases and a reduced- order extrapolation SFDS with lower dimensions and sufficiently high accuracy for the 3D parabolic equation is established. Third, the error estimates between the classical SFDS solutions and the reduced-order extrapolation SFDS solutions and the implementation for solving the reduced-order extrapolation SFDS are provided. Finally, a numerical example shows that the errors of numerical computations are consistent with the theoretical results. Moreover, it is shown that the reduced-order extrapolation SFDS is effective and feasible to find the numerical solutions for the 3D parabolic equation.
文摘In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust sample autocorrelations based on ranks and signsto obtain estimators for the parameters of the Poisson INAR(1) process. The effects ofadditive outliers on the estimates of parameters of integer-valued time series are examined. Some numerical results of the estimators are presented with a discussion of theobtained results. The proposed methods are applied to a dataset concerning the numberof different IP addresses accessing the server of the pages of the Department of Statistics of the University of Würzburg. The results presented here give motivation to use themethodology in practical situations in which Poisson INAR(1) process contains additiveoutliers.
