Uncertainty Relation Revisited from Quantum Estimation Theory

By invoking quantum estimation theory we formulate bounds of errors in quantum measurement for arbitrary quantum states and observables in a finite-dimensional Hilbert space. We prove that the measurement errors of two observables satisfy Heisenberg's uncertainty relation, find the attainable bound, and provide a strategy to achieve it.Comment: manuscript including 4 pages and 2 figure

Linear estimation in Krein spaces. Part II. Applications

We have shown that several interesting problems in H∞-filtering, quadratic game theory, and risk sensitive control and estimation follow as special cases of the Krein-space linear estimation theory developed in Part I. We show that all these problems can be cast into the problem of calculating the stationary point of certain second-order forms, and that by considering the appropriate state space models and error Gramians, we can use the Krein-space estimation theory to calculate the stationary points and study their properties. The approach discussed here allows for interesting generalizations, such as finite memory adaptive filtering with varying sliding patterns

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Estimation theory

Theory and Applications of Proper Scoring Rules

We give an overview of some uses of proper scoring rules in statistical inference, including frequentist estimation theory and Bayesian model selection with improper priors.Comment: 13 page

Online Drift Compensation for Chemical Sensors Using Estimation Theory

Sensor drift from slowly changing environmental conditions and other instabilities can greatly degrade a chemical sensor\u27s performance, resulting in poor identification and analyte quantification. In the present work, estimation theory (i.e., various forms of the Kalman filter) is used for online compensation of baseline drift in the response of chemical sensors. Two different cases, which depend on the knowledge of the characteristics of the sensor system, are studied. First, an unknown input is considered, which represents the practical case of analyte detection and quantification. Then, the more general case, in which the sensor parameters and the input are both unknown, is studied. The techniques are applied to simulated sensor data, for which the true baseline and response are known, and to actual liquid-phase SH-SAW sensor data measured during the detection of organophosphates. It is shown that the technique is capable of estimating the baseline signal and recovering the true sensor signal due only to the presence of the analyte. This is true even when the baseline drift changes rate or direction during the detection process or when the analyte is not completely flushed from the system

Network Coding: Connections Between Information Theory And Estimation Theory

In this paper, we prove the existence of fundamental relations between information theory and estimation theory for network-coded flows. When the network is represented by a directed graph G=(V, E) and under the assumption of uncorrelated noise over information flows between the directed links connecting transmitters, switches (relays), and receivers. We unveil that there yet exist closed-form relations for the gradient of the mutual information with respect to different components of the system matrix M. On the one hand, this result opens a new class of problems casting further insights into effects of the network topology, topological changes when nodes are mobile, and the impact of errors and delays in certain links into the network capacity which can be further studied in scenarios where one source multi-sinks multicasts and multi-source multicast where the invertibility and the rank of matrix M plays a significant role in the decoding process and therefore, on the network capacity. On the other hand, it opens further research questions of finding precoding solutions adapted to the network level.Comment: IEEE Wireless Communications and Networking Conference (WCNC), April, 201

Quantum estimation theory

Bibliography: p. 10."January 1979."Supported by NSF Grant ENG76-02860 NSF Grant ENG77-28444by Sanjoy K. Mitter, Stephen K. Young