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Parameters estimation by fitting correlation functions of continuous quantum measurement
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We propose a simple method to estimate the parameters of a continuously measured quantum system, by fitting correlation functions of the measured signal. We demonstrate the approach in simulation, both on toy examples and on a recent superconducting circuits experiment which proved particularly difficult to characterise using conventional methods. The idea is applicable to any system whose evolution is described by a jump or diffusive stochastic master equation. It allows the simultaneous estimation of many parameters, is practical for everyday use, is suitable for large Hilbert space dimensions, and takes into account experimental constraints such as detector imperfections and signal filtering and digitisation. Unlike existing methods, it also provides a direct way to understand how each parameter is estimated from the measured signal. This makes the approach interpretable, facilitates debugging, and enables validating the adequacy of a model with the observed data.
Forward citations
Cited by 3 Pith papers
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Time-dependent multiparameter estimation for quantum experiments via online-offline sequential Monte-Carlo method
Hybrid online-offline SMC with batch-averaged Kraus maps estimates time-dependent multiparameters from noisy continuous quantum measurements better than calibration on two superconducting-qubit datasets.
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Time-averaged continuous quantum measurement
The quantum state conditioned on time-averaged measurement records is updated exactly by a Gaussian-averaged tilted-Liouvillian superoperator, which can be expanded to arbitrary order in sqrt(Delta t).
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The role of correlations in a sequence of quantum observations on empirical measures
For general sequential quantum measurements, the paper derives asymptotic covariance matrices of empirical distributions of outcome substrings and a relative-entropy measure of the influence of correlations.
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