Zhang, K;
Cong, S;
Ding, J;
Zhang, J;
Li, K;
(2019)
Efficient and Fast Optimization Algorithms for Quantum State Filtering and Estimation.
In:
Proceedings of the 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP).
(pp. pp. 7-13).
IEEE: Marrakesh, Morocco.
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Abstract
In this paper, based on Alternating Direction Multiplier Method (ADMM) and Compressed Sensing (CS), we develop three types of novel convex optimization algorithms for the quantum state estimation and filtering. Considering sparse state disturbance and measurement noise simultaneously, we propose a quantum state filtering algorithm. At the same time, the quantum state estimation algorithms for either sparse state disturbance or measurement noise are proposed, respectively. Contrast with other algorithms in literature, simulation experiments verify that all three algorithms have low computational complexity, fast convergence speed and high estimation accuracy at lower measurement rates.
Type: | Proceedings paper |
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Title: | Efficient and Fast Optimization Algorithms for Quantum State Filtering and Estimation |
Event: | 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) |
ISBN-13: | 9781728100159 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/ICICIP47338.2019.9012097 |
Publisher version: | https://ieeexplore.ieee.org/document/9012097 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | quantum state estimation and filtering, Alternating Direction Multiplier Method, convex optimization algorithm |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
URI: | https://discovery.ucl.ac.uk/id/eprint/10096923 |
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