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It is challenged only recently that the precision attainable in any measurement of a physical parameter is fundamentally limited by the quantum Cram{e}r-Rao Bound (QCRB). Here, targeting at measuring parameters in strongly dissipative systems, we propose an innovative measurement scheme called {it dissipative adiabatic measurement} and theoretically show that it can beat the QCRB. Unlike projective measurements, our measurement scheme, though consuming more time, does not collapse the measured state and, more importantly, yields the expectation value of an observable as its measurement outcome, which is directly connected to the parameter of interest. Such a direct connection {allows to extract} the value of the parameter from the measurement outcomes in a straightforward manner, with no fundamental limitation on precision in principle. Our findings not only provide a marked insight into quantum metrology but also are highly useful in dissipative quantum information processing.
The variance and the entropy power of a continuous random variable are bounded from below by the reciprocal of its Fisher information through the Cram{e}r-Rao bound and the Stams inequality respectively. In this note, we introduce the Fisher informat
This is a tutorial aimed at illustrating some recent developments in quantum parameter estimation beyond the Cram`er-Rao bound, as well as their applications in quantum metrology. Our starting point is the observation that there are situations in cla
We examine the role of information geometry in the context of classical Cramer-Rao (CR) type inequalities. In particular, we focus on Eguchis theory of obtaining dualistic geometric structures from a divergence function and then applying Amari-Nagoak
Single molecule localization microscopy has the potential to resolve structural details of biological samples at the nanometer length scale. However, to fully exploit the resolution it is crucial to account for the anisotropic emission characteristic
In this paper, we analyze the impact of compressed sensing with complex random matrices on Fisher information and the Cram{e}r-Rao Bound (CRB) for estimating unknown parameters in the mean value function of a complex multivariate normal distribution.