21 results found
Zhang J, Cong S, Ling Q, et al., 2019, An Efficient and Fast Quantum State Estimator With Sparse Disturbance, IEEE TRANSACTIONS ON CYBERNETICS, Vol: 49, Pages: 2546-2555, ISSN: 2168-2267
Li K, Chandrasekera TC, Li Y, et al., 2018, A Non-Linear Reweighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography, IEEE SENSORS JOURNAL, Vol: 18, Pages: 5049-5057, ISSN: 1530-437X
Li K, Zheng K, Yang J, et al., 2017, Hybrid reconstruction of quantum density matrix: when low-rank meets sparsity, Quantum Information Processing, Vol: 16, ISSN: 1570-0755
Both the mathematical theory and experiments have verified that the quantumstate tomography based on compressive sensing is an efficient framework for thereconstruction of quantum density states. In recent physical experiments, we foundthat many unknown density matrices in which people are interested in are low-rankas well as sparse. Bearing this information in mind, in this paper we propose a reconstructionalgorithm that combines the low-rank and the sparsity property of densitymatrices and further theoretically prove that the solution of the optimization functioncan be, and only be, the true density matrix satisfying the model with overwhelmingprobability, as long as a necessary number of measurements are allowed. The solverleverages the fixed-point equation technique in which a step-by-step strategy is developedby utilizing an extended soft threshold operator that copes with complex values.Numerical experiments of the density matrix estimation for real nuclear magnetic resonancedevices reveal that the proposed method achieves a better accuracy comparedto some existing methods. We believe that the proposed method could be leveraged asa generalized approach and widely implemented in the quantum state estimation.
Yang J, Cong S, Liu X, et al., 2017, Effective quantum state reconstruction using compressed sensing in NMR quantum computing, Physical Review A, Vol: 96, ISSN: 1050-2947
Compressed sensing (CS) has been verified as an effective technique in the reconstruction of quantum state; however, it is still unknown if CS can reconstruct quantum states given the incomplete data measured by nuclear magnetic resonance (NMR). In this paper, we propose an effective NMR quantum state reconstruction method based on CS. Different from the conventional CS-based quantum state reconstruction, our method uses the actual observation data from NMR experiments rather than the data measured by the Pauli operators. We implement measurements on quantum states in practical NMR computing experiments and reconstruct states of two, three, and four qubits using fewer number of measurement settings, respectively. The proposed method is easy to implement and performs more efficiently with the increase of the system dimension size. The performance reveals both efficiency and accuracy, which provides an alternative for the quantum state reconstruction in practical NMR.
Cong S, Wen J, Meng F, et al., 2017, Global Stabilization of Mixed States for Stochastic Quantum Systems via Switching Control, of 20th World Congress of the International Federation of Automatic Control (IFAC), Publisher: Elsevier, Pages: 13032-13037, ISSN: 1474-6670
The global stabilization of mixed states for finite dimensional stochastic quantum systems with non-regular measurement operator and non-diagonal free Hamiltonian is investigated in this paper. A two-part switching control strategy is proposed, in which the constant control is used to steer the system state to enter the convergence domain, while the control law which is designed based on Lyapunov stability theorem is used to attract the system state in convergence domain to the target state. The convergence of the switching control is strictly proved. Moreover, the numerical experiments on a three dimensional stochastic quantum system are implemented to demonstrate the effectiveness of the control proposed.
Zhang J, Li K, Cong S, et al., 2017, Efficient reconstruction of density matrices for high dimensional quantum state tomography, Signal Processing, Vol: 139, Pages: 136-142, ISSN: 0165-1684
Li K, Zhang J, Cong S, 2017, Fast reconstruction of high-qubit-number quantum states via low-rate measurements, PHYSICAL REVIEW A, Vol: 96, ISSN: 2469-9926
Zheng K, Li K, Cong S, 2016, A reconstruction algorithm for compressive quantum tomography using various measurement sets., Sci Rep, Vol: 6
Compressed sensing (CS) has been verified that it offers a significant performance improvement for large quantum systems comparing with the conventional quantum tomography approaches, because it reduces the number of measurements from O(d2) to O(rd log(d)) in particular for quantum states that are fairly pure. Yet few algorithms have been proposed for quantum state tomography using CS specifically, let alone basis analysis for various measurement sets in quantum CS. To fill this gap, in this paper an efficient and robust state reconstruction algorithm based on compressive sensing is developed. By leveraging the fixed point equation approach to avoid the matrix inverse operation, we propose a fixed-point alternating direction method algorithm for compressive quantum state estimation that can handle both normal errors and large outliers in the optimization process. In addition, properties of five practical measurement bases (including the Pauli basis) are analyzed in terms of their coherences and reconstruction performances, which provides theoretical instructions for the selection of measurement settings in the quantum state estimation. The numerical experiments show that the proposed algorithm has much less calculating time, higher reconstruction accuracy and is more robust to outlier noises than many existing state reconstruction algorithms.
Harraz S, Yang J, Li K, et al., 2016, Quantum state transfer control based on the optimal measurement, Optimal Control Applications and Methods, Vol: 38, Pages: 744-753, ISSN: 0143-2087
This paper explores the optimal control of quantum state transfer in a two-dimensional quantum system by a sequence of non-selective projection measurements. We show that for a given initial state, one can always find the corresponding projection operator that can effectively drive the given initial state to any arbitrary target pure state. An external control field is proposed to compensate the effect of the free evolution of system. Numerical simulations and characteristics analysis are given in three cases: without considering free evolution, considering free evolution, and with the action of external control field. The simulating experimental results show that the optimal measurement control is more effective by using proposed external control field.
Ma R, Liu E, Wang R, et al., 2016, Energy-Aware Preferential Attachment Model for Wireless Sensor Networks with Improved Survivability, KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, Vol: 10, Pages: 3066-3079, ISSN: 1976-7277
Li K, Zhang H, Kuang S, et al., 2016, An improved robust ADMM algorithm for quantum state tomography, QUANTUM INFORMATION PROCESSING, Vol: 15, Pages: 2343-2358, ISSN: 1570-0755
Li K, Rojas CR, Yang T, et al., 2016, Piecewise Sparse Signal Recovery Via Piecewise Orthogonal Matching Pursuit, IEEE 41th International Conference on Acoustics, Speech and Signal Processing, (ICASSP 2016), Publisher: IEEE, ISSN: 2379-190X
In this paper, we consider the recovery of piecewise sparse signals from incomplete noisy measurements via a greedy algorithm. Here piecewise sparse means that the signal can be approximated in certain domain with known number of nonzero entries in each piece/segment. This paper makes a two-fold contribution to this problem: 1) formulating a piecewise sparse model in the framework of compressed sensing and providing the theoretical analysis of corresponding sensing matrices; 2) developing a greedy algorithm called piecewise orthogonal matching pursuit (POMP) for the recovery of piecewise sparse signals. Experimental simulations verify the effectiveness of the proposed algorithms.
Li K, Sundin M, Rojas CR, et al., 2015, Alternating strategies with internal ADMM for low-rank matrix reconstruction, Signal Processing, Vol: 121, Pages: 153-159, ISSN: 0165-1684
This paper focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover low-rank matrices with linear parameterized structures, such as Hankel matrices. The use of ADMM helps to improve the estimate in each iteration due to its capability of incorporating information about the direction of estimates achieved in previous iterations. We show that merging these two alternating strategies leads to a better performance and less consumed time than the existing alternating least squares (ALS) strategy. The improved performance is verified via numerical simulations with varying sampling rates and real applications.
Li K, Rojas C, Chatterjee S, et al., Piecewise Toeplitz matrices-based sensing for rank minimization, IEEE European Signal Processing Conference (EUSIPCO 2014)
Yang T, Yuan Y, Li K, et al., Finite-time road grade computation for a vehicle platoon, IEEE Conference on Decision and Control (CDC 2014)
Li K, Cong S, A robust compressive quantum state tomography algorithm using ADMM, 19th World Congress of the International Federation of Automation Control (IFAC 2014)
Li K, Gan L, Ling C, 2013, Convolutional Compressed Sensing Using Deterministic Sequences, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 61, ISSN: 1053-587X
Li K, 2013, Structured Compressed Sensing Using Deterministic Sequences
Gan L, Li K, Ling C, 2012, Golay Meets Hadamard: Golay-Paired Hadamard Matrices for Fast Compressed Sensing, IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 637-641
Li K, Ling C, Gan L, 2011, DETERMINISTIC COMPRESSED-SENSING MATRICES: WHERE TOEPLITZ MEETS GOLAY, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3748-3751, ISSN: 1520-6149
Li K, Ling C, Gan L, 2009, Statistical Restricted Isometry Property of Orthogonal Symmetric Toeplitz Matrices, IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 183-187
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.