- Open Access
Quantum Rauch-Tung-Striebel smoothed state
Phys. Rev. Research 3, 033196 – Published 27 August, 2021
DOI: https://doi.org/10.1103/PhysRevResearch.3.033196
Abstract
Smoothing is a technique that is used to estimate the state of a system using measurement information both prior and posterior to the estimation time. Two notable examples of this technique are the Rauch-Tung-Striebel and Mayne-Fraser-Potter smoothing techniques for linear Gaussian systems, both resulting in the optimal smoothed estimate of the state. However, when considering a quantum system, classical smoothing techniques can result in an estimate that is not a valid quantum state. Consequently, a different smoothing theory was developed explicitly for quantum systems. This theory has since been applied to the special case of linear Gaussian quantum (LGQ) systems, where, in deriving the LGQ state smoothing equations, the Mayne-Fraser-Potter technique was utilized. As a result, the final equations describing the smoothed state are closely related to the classical Mayne-Fraser-Potter smoothing equations. In this paper, I derive the equivalent Rauch-Tung-Striebel form of the quantum state smoothing equations, which further simplify the calculation for the smoothed quantum state in LGQ systems, and I provide insight into the dynamics of the smoothed quantum state. This form of the LGQ smoothing equations brings to light a property of the smoothed quantum state that was hidden in the Mayne-Fraser-Potter form, namely the nondifferentiability of the smoothed mean. By identifying the nondifferentiable part of the smoothed mean, I am then able to derive a necessary and sufficient condition for the quantum smoothed mean to be differentiable in the steady-state regime.
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References (33)
- R. E. Kalman and R. S. Bucy, New results in linear filtering and prediction theory, J. Basic Eng. 83, 95 (1961).
- H. E. Rauch, Solutions to the linear smoothing problem, IEEE Trans. Autom. Contr. 8, 371 (1963).
- H. E. Rauch, F. Tung, and C. T. Striebel, Maximum likelihood estimates of linear dynamic systems, AIAA J. 3, 1445 (1965).
- D. Q. Mayne, A solution of the smoothing problem for linear dynamic systems, Automatica 4, 73 (1966).
- D. C. Fraser, A new technique for the optimal smoothing of data, Ph.D. thesis, Massachusetts Institute of Technology, 1967.
- D. Fraser and J. Potter, The optimum linear smoother as a combination of two optimum linear filters, IEEE Trans. Autom. Contr. 14, 387 (1969).
- H. L. Weinert, Fixed Interval Smoothing for State Space Models (Kluwer Academic, New York, 2001).
- S. Haykin, Kalman Filtering and Neural Networks (Wiley, New York, 2001).
- H. L. Van Trees and K. L. Bell, Detection, Estimation, and Modulation Theory, Part I: Detection, Estimation, and Filtering Theory, 2nd ed. (Wiley, New York, 2013).
- R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, 4th ed. (Wiley, New York, 2012).
- G. A. Einicke, Smoothing, Filtering and Prediction: Estimating the Past, Present and Future (InTech Rijeka, Rijeka, 2012).
- B. Friedland, Control System Design: An Introduction to State-space Methods (Courier, New York, 2012).
- S. Särkkä, Bayesian Filtering and Smoothing (Cambridge University Press, Cambridge, 2013), Vol. 3.
- V. P. Belavkin, Information, Complexity and Control in Quantum Physics, edited by A. Blaquíere, S. Dinar, and G. Lochak (Springer, New York, 1987).
- V. P. Belavkin, Quantum continual measurements and a posteriori collapse on ccr, Commun. Math. Phys. 146, 611 (1992).
- V. P. Belavkin, Measurement, filtering and control in quantum open dynamical systems, Rep. Math. Phys. 43, A405 (1999).
- A. C. Doherty and K. Jacobs, Feedback control of quantum systems using continuous state estimation, Phys. Rev. A 60, 2700 (1999).
- H. M. Wiseman and A. C. Doherty, Optimal Unravellings for Feedback Control in Linear Quantum Systems, Phys. Rev. Lett. 94, 070405 (2005).
- H. M. Wiseman and G. J. Milburn, Quantum Measurement and Control (Cambridge University Press, Cambridge, England, 2010).
- M. Tsang, Optimal waveform estimation for classical and quantum systems via time-symmetric smoothing, Phys. Rev. A 80, 033840 (2009).
- S. Gammelmark, B. Julsgaard, and K. Mølmer, Past Quantum States of a Monitored System, Phys. Rev. Lett. 111, 160401 (2013).
- I. Guevara and H. M. Wiseman, Quantum State Smoothing, Phys. Rev. Lett. 115, 180407 (2015).
- K. Ohki, A smoothing theory for open quantum systems: The least mean square approach, in 54th IEEE Conference on Decision and Control (CDC) (IEEE, Piscataway, NJ, 2015), pp. 4350–4355.
- K. T. Laverick, A. Chantasri, and H. M. Wiseman, General criteria for quantum state smoothing with necessary and sufficient criteria for linear gaussian quantum systems, Quantum Stud.: Math. Found. 8, 37 (2021).
- A. Chantasri, I. Guevara, and H. M. Wiseman, Quantum state smoothing: why the types of observed and unobserved measurements matter, New J. Phys. 21, 083039 (2019).
- A. Chantasri, I. Guevara, K. T. Laverick, and H. M. Wiseman, Unifying theory of quantum state estimation using past and future information, Phys. Rep. 930, 1 (2021).
- K. T. Laverick, A. Chantasri, and H. M. Wiseman, Quantum State Smoothing for Linear Gaussian Systems, Phys. Rev. Lett. 122, 190402 (2019).
- K. T. Laverick, A. Chantasri, and H. M. Wiseman, Linear Gaussian quantum state smoothing: Understanding the optimal unravelings for Alice to estimate Bob's state, Phys. Rev. A 103, 012213 (2021).
- T. Kailath and P. Frost, An innovations approach to least-squares estimation–part ii: Linear smoothing in additive white noise, IEEE Trans. Autom. Contr. 13, 655 (1968).
- T. Kailath, The innovations approach to detection and estimation theory, Proc. IEEE 58, 680 (1970).
- T. Kailath, Some new algorithms for recursive estimation in constant linear systems, IEEE Trans. Inf. Theor. 19, 750 (1973).
- F. Badawi, A. Lindquist, and M. Pavon, A stochastic realization approach to the smoothing problem, IEEE Trans. Autom. Contr. 24, 878 (1979).
- A. Chia and H. M. Wiseman, Complete parametrizations of diffusive quantum monitorings, Phys. Rev. A 84, 012119 (2011).