Efficient estimation algorithm for ARMA, exponential and other trigonometric model with quantum constraints

A new estimation algorithm has been developed here, which refers to covariance shaping least square estimation (CSLS) based on the quantum mechanical concepts and constraints. The algorithm has been applied to ARMA, complex exponential, sine, cosine and sinc models with various parameter values. The same models can be applied with white Gaussian noise, which estimates the bias in the parameter and the validity of the uncertainty can be analysed. For optimal quantum measurement design, the performance of the CSLS estimator is developed, discussed and compared with LS, Shrunken and Ridge estimators for different applications. The results suggest that the CSLS estimator can outperform from others at low-to-moderate signal-to-noise ratio.