Review of the Applications of Kalman Filtering in Quantum Systems

State variable and parameter estimations are important for signal sensing and feedback control in both traditional engineering systems and quantum systems. The Kalman filter, which is one of the most popular signal recovery techniques in classical systems for decades, has now been connected to the s...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 14; no. 12; p. 2478
Main Authors Ma, Kezhao, Kong, Jia, Wang, Yihan, Lu, Xiao-Ming
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2022
Subjects
Evolution
Feedback control
Feedback control systems
Field programmable gate arrays
Kalman filter
Kalman filters
Magnetic fields
Mathematical models
Mechanical systems
Nanoparticles
Nonlinear systems
Parameter estimation
Probability
Quantum mechanics
Quantum theory
Signal reconstruction
stochastic filter
stochastic master equation
Variables
Germany
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Summary:State variable and parameter estimations are important for signal sensing and feedback control in both traditional engineering systems and quantum systems. The Kalman filter, which is one of the most popular signal recovery techniques in classical systems for decades, has now been connected to the stochastic master equations of linear quantum mechanical systems. Various studies have invested effort on mapping the state evolution of a quantum system into a set of classical filtering equations. However, establishing proper evolution models with symmetry to classical filter equation for quantum systems is not easy. Here, we review works that have successfully built a Kalman filter model for quantum systems and provide an improved method for optimal estimations. We also discuss a practical scenario involving magnetic field estimations in quantum systems, where non-linear Kalman filters could be considered an estimation solution.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14122478