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

Khaled Obaideen; Mohammad AlShabi; S. Andrew Gadsden; Talal Bonny

doi:10.1117/12.3015939

7 June 2024 Quantum tomographic reconstruction: a Bayesian approach using the extended Kalman filter

Khaled Obaideen, Mohammad AlShabi, S. Andrew Gadsden, Talal Bonny

Proceedings Volume 13028, Quantum Information Science, Sensing, and Computation XVI; 130280K (2024) https://doi.org/10.1117/12.3015939
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Khaled Obaideen, Mohammad AlShabi, S. Andrew Gadsden, and Talal Bonny "Quantum tomographic reconstruction: a Bayesian approach using the extended Kalman filter", Proc. SPIE 13028, Quantum Information Science, Sensing, and Computation XVI, 130280K (7 June 2024); https://doi.org/10.1117/12.3015939

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