Online Optimization Algorithm of the Quantum State Filter With Disturbance and Noise and Its Convergence Study

An online quantum state filter (OQSF) optimization algorithm in the existence of sparse disturbance and Gaussian noise is proposed in this article. The optimization algorithm is based on the continuous weak measurements (CWMs) and the online measurement record sequence constructed. The OQSF problem is decomposed into three subproblems, and three variables are estimated in real-time at the same time. The convergence OQSF algorithm proposed is strictly proved by means of two lemmas proof of the lower bound function of the distance between the optimal solution and the original one having sufficient decreasing space. By comparing the performances with existing algorithms, the developed OQSF algorithm proposed demonstrates advantages in the numerical performance comparisons.