Dynamical parameter estimation using realistic photodetection

Warszawski, Prahlad; Gambetta, Jay; Wiseman, Howard Mark

doi:10.1103/physreva.69.042104

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…More generally, we may wish to estimate one or more numbers that parameterize the state, Hamiltonian, or overall evolution of a system. Such parameters can be estimated by making a continuous measurement on an evolving system and processing the measurement results [332][333][334][335][336][337]. Such a procedure has applications to metrology, such as the detection of weak force by monitoring a harmonic oscillator [336], or estimating the Rabi frequency of a two-level atom [333].…”

Section: Quantum Parameter Estimationmentioning

confidence: 99%

Quantum feedback: Theory, experiments, and applications

et al. 2017

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The control of individual quantum systems is now a reality in a variety of physical settings. Feedback control is an important class of control methods because of its ability to reduce the effects of noise. In this review we give an introductory overview of the various ways in which feedback may be implemented in quantum systems, the theoretical methods that are currently used to treat it, the experiments in which it has been demonstrated to-date, and its applications. In the last few years there has been rapid experimental progress in the ability to realize quantum measurement and control of mesoscopic systems. We expect that the next few years will see further rapid advances in the precision and sophistication of feedback control protocols realized in the laboratory.

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Section: Quantum Parameter Estimationmentioning

confidence: 99%

Quantum feedback: Theory, experiments, and applications

et al. 2017

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“…It is possible to formulate extended Kalman filters for such scenarios, in which one or more parameters appearing in the Hamiltonian are treated as static or dynamic variables to be estimated from a continuous measurement signal. Some general investigations have appeared on the sensitivity and optimization of such procedures (including analytic studies in the Gaussian framework and numerical studies allowing more general likelihood functions) [130][131][132]. A thorough analysis has been performed of using this strategy for broadband magnetometry with atoms [10], and it has been shown that real-time feedback can be exploited for significant gains in robustness.…”

Section: Quantum System Identificationmentioning

confidence: 99%

Principles and applications of control in quantum systems

Mabuchi

Khaneja

2005

Intl J Robust & Nonlinear

153

122

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SUMMARYWe describe in this article some key themes that emerged during a Caltech/AFOSR Workshop on 'Principles and Applications of Control in Quantum Systems' (PRACQSYS), held 21-24 August 2004 at the California Institute of Technology. This workshop brought together engineers, physicists and applied mathematicians to construct an overview of new challenges that arise when applying constitutive methods of control theory to nanoscale systems whose behaviour is manifestly quantum. Its primary conclusions were that the number of experimentally accessible quantum control systems is steadily growing (with a variety of motivating applications), that appropriate formal perspectives enable straightforward application of the essential ideas of classical control to quantum systems, and that quantum control motivates extensive study of model classes that have previously received scant consideration.

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“…Finally, we combine these theories to consider the following case: a quantum system is monitored continuously in time by a classical system but we only have access to the results of non-ideal measurements performed on the classical system. Note that such joint systems have recently been studied by Warszawski et al [21,22,23] and Oxtoby et al [24]. Warszawski et al considered continuous-in-time monitoring of a quantum optical system with realistic photodections while Oxtoby et al considered continuous-in-time monitoring of a quantum solid-state system with a quantum point contact.…”

Section: Introductionmentioning

confidence: 99%

Stochastic simulations of conditional states of partially observed systems, quantum and classical

Gambetta

Wiseman

2005

J. Opt. B: Quantum Semiclass. Opt.

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In a partially observed quantum or classical system the information that we cannot access results in our description of the system becoming mixed, even if we have perfect initial knowledge. That is, if the system is quantum the conditional state will be given by a state matrix ρr(t), and if classical, the conditional state will be given by a probability distribution Pr(x, t), where r is the result of the measurement. Thus to determine the evolution of this conditional state, under continuous-in-time monitoring, requires a numerically expensive calculation. In this paper we demonstrate a numerical technique based on linear measurement theory that allows us to determine the conditional state using only pure states. That is, our technique reduces the problem size by a factor of N , the number of basis states for the system. Furthermore we show that our method can be applied to joint classical and quantum systems such as arise in modeling realistic (finite bandwidth, noisy) measurement.

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Dynamical parameter estimation using realistic photodetection

Cited by 6 publications

References 24 publications

Quantum feedback: Theory, experiments, and applications

Quantum feedback: Theory, experiments, and applications

Principles and applications of control in quantum systems

Stochastic simulations of conditional states of partially observed systems, quantum and classical

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