Abstract In this paper, we propose a quantum amplitude estimation method that uses modified Grover operator and quadratically improves the accuracy in ideal case, as conventional one using standard operator. Under depolarizing noise, proposed can outperform sense it principle achieve ultimate characterized by Fisher information limit of large number qubits, while cannot same value accuracy. gen...

We design and analyze two new low depth algorithms for amplitude estimation (AE) achieving an optimal tradeoff between the quantum speedup circuit depth. For β∈(0,1]...

We devise a new approach for non-parametric adaptive spectral analysis method, which is called the Adaptive Tuning Amplitude and Phase Estimation (ATAPES) method. The main advantage of the ATAPES algorithm is its elimination of biased estimation results in APES method, which is biased peak location and corresponding biased amplitude estimation problem. Therefore, ATAPES method provides more acc...

We investigate herein the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise. A relatively large number of amplitude estimators are described which encompass Least Squares (LS) and Weighted Least Squares (WLS) methods. Additionally, lterbank approaches, which are widely used for spectral analysis, are extended to amplitude estimation. Speci cally,...

We present a multichannel, model-free method for estimation of event-related potential (ERP) amplitude ratios and amplitudes using singular value decomposition (SVD), and compare with the Dipole Components Model (DCM). When the ERPs are generated by a single or multiple dipoles with equal amplitude ratios, the SVD method is superior to DCM in terms of reliable estimation of amplitude and is com...

Anomaly detection, as an important branch of machine learning, plays a critical role in fraud health care, intrusion military surveillance, etc. An anomaly detection algorithm based on density estimation (called ADDE algorithm) is one the widely used algorithms. However, computationally expensive when processing big data sets. To solve this problem, paper, we propose efficient quantum amplitude...

based on the convolutional model, a seismic trace is the convolution of seismic source wavelet and reflection coefficient series of the earth. seismic source wavelet estimation is one of the most important stages in processing and interpretation of seismic data. accurate estimation of wavelet increases the efficiency of the deconvolution and temporal resolution of seismic data. on the other han...