Fuzzy wavelet network based on extended Kalman filter training algorithm combined with least square weight estimation: Efficient and improved chromatographic QSRR/QSPR models

Jalili‐Jahani, Nasser; Zeraatkar, Ehsan

doi:10.1016/j.chemolab.2020.104191

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Traditional quantum state reconstruction methods often struggle with the high dimensionality and the presence of noise, leading to significant challenges in obtaining accurate and reliable state estimations. In response to these challenges, this paper proposes a novel approach to quantum tomographic reconstruction that leverages the Extended Kalman Filter (EKF) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], a method renowned for its efficacy in non-linear state estimation problems in various engineering and scientific domains. The EKF, an advancement over the classical Kalman Filter , is well-suited for addressing the nonlinear dynamics and uncertainties inherent in quantum systems.…”

Section: Introductionmentioning

confidence: 99%

Quantum tomographic reconstruction: a Bayesian approach using the extended Kalman filter

Obaideen,

AlShabi,

Gadsden

et al. 2024

Quantum Information Science, Sensing, and Computation XVI

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This study presents a comprehensive bibliometric analysis of the fusion between quantum tomography and the Extended Kalman Filter (EKF), emphasizing its superiority in refining quantum tomographic reconstructions compared to conventional methodologies. By intersecting quantum mechanical principles with sophisticated filtering technologies, our analysis uncovers emergent research trajectories within the domain of quantum information science. It underscores the significant potential that this integration holds for the evolution of quantum technology applications. Furthermore, this paper delineates the expansive impact of improved quantum state information across a spectrum of scientific fields, thereby enriching the discourse on quantum state estimation and its applications. Through this investigation, we contribute to a deeper understanding of the pivotal role that advanced filtering techniques, specifically the EKF, play in advancing quantum tomography, paving the way for future innovations in quantum computing and beyond.

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