Title :
An improved quantum state estimation algorithm via compressive sensing
Author :
Cong, S. ; Zhang, H. ; Li, K.
Author_Institution :
Univ. of Sci. & Technol. of China, Hefei, China
Abstract :
The dimension of the density matrix of a quantum system increases with the qubits of the quantum system, which makes the quantum state estimation time consuming and requires huge computation. In order to reduce the computational time in quantum state estimation, the problem of quantum state estimation based on compressive sensing is changed to the optimization problem with error constraint. In this paper, an improved Alternating Direction Method of Multipliers (ADMM) algorithm is proposed to design the optimization scheme of solving the pure state of quantum state estimation in the cases of with and without external noise. The experiments are implemented in the MATLAB. The comparison results between adaptive and fixed weight value indicate that the improved algorithm has better performances in both aspects of estimation accuracy and robustness to external disturbances. We also extend the quantum state estimation to the qubits of six and seven.
Keywords :
compressed sensing; matrix algebra; quantum computing; state estimation; ADMM algorithm; MATLAB; alternating direction method of multipliers; compressive sensing; density matrix; error constraint; optimization problem; quantum state estimation algorithm; quantum system; Compressed sensing; Estimation error; Measurement uncertainty; Noise; Optimization; State estimation;
Conference_Titel :
Robotics and Biomimetics (ROBIO), 2014 IEEE International Conference on
DOI :
10.1109/ROBIO.2014.7090687