Harraz, S;
Cong, S;
Li, K;
(2021)
Online quantum state tomography of N-qubit via continuous weak measurement and compressed sensing.
International Journal of Quantum Information
, 19
(8)
, Article 2040006. 10.1142/S0219749920400067.
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Abstract
N-qubit quantum system, which are indirectly acted on the quantum system, are derived according to the measurement operator results of two-level quantum system. The online time-varying measurement operators are obtained by means of the dynamic evolution equation of the system. The quantum state is online estimated by solving the optimization problem of minimizing the two-norm with the positive definite constraints of density matrix, and we use the nonnegative least squares algorithm to solve the optimization problem. CS theory is used to reduce the number of the measurements in the process of online state estimation. In the numerical experiments, we study the effects of external control field, measurement rate and the different numbers of qubits on the performance in the proposed method. The minimum required numbers of measurements for 2, 3, 4 and 5 qubits are found. The normalized distance and fidelity of our proposed method can achieve satisfying accuracy with small number of measurements.
| Type: | Article |
|---|---|
| Title: | Online quantum state tomography of N-qubit via continuous weak measurement and compressed sensing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1142/S0219749920400067 |
| Publisher version: | https://doi.org/10.1142/S0219749920400067 |
| 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, quantum state tomography, compressed sensing, quantum weak measurement, convex optimization |
| UCL classification: | UCL 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 > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117789 |
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