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Bounding overwatch
About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.
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TL;DR: In this article , a variational quantum algorithm called Variational Quantum Fisher Information Estimation (VQFIE) is proposed to estimate lower and upper bounds on the QFI, based on bounding the fidelity.
Abstract: The Quantum Fisher information (QFI) quantifies the ultimate precision of estimating a parameter from a quantum state, and can be regarded as a reliability measure of a quantum system as a quantum sensor. However, estimation of the QFI for a mixed state is in general a computationally demanding task. In this work we present a variational quantum algorithm called Variational Quantum Fisher Information Estimation (VQFIE) to address this task. By estimating lower and upper bounds on the QFI, based on bounding the fidelity, VQFIE outputs a range in which the actual QFI lies. This result can then be used to variationally prepare the state that maximizes the QFI, for the application of quantum sensing. In contrast to previous approaches, VQFIE does not require knowledge of the explicit form of the sensor dynamics. We simulate the algorithm for a magnetometry setup and demonstrate the tightening of our bounds as the state purity increases. For this example, we compare our bounds to literature bounds and show that our bounds are tighter.
11 citations
01 Jan 1994
11 citations
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TL;DR: In this article, a review of lower bounding methodologies for lot-sizing production/inventory models is performed for both dynamic discrete time demand and continuous review, constant demand situations, where lower bounds are an essential ingredient in the evaluation of heuristics when optima are unknown or too computationally complex to comfortably evaluate.
Abstract: A review is undertaken of lower bounding methodologies for lot-sizing production/inventory models. This is done for both dynamic discrete time demand and continuous review, constant demand situations. The view is taken that lower bounds are an essential ingredient in the evaluation of heuristics when optima are unknown or too computationally complex to comfortably evaluate. Several directions of future research are suggested.
11 citations
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01 Jan 2018TL;DR: The Rectangular Bounding Process (RBP) as discussed by the authors is a nonparametric partition prior on a hypercube that can be directly extended from a finite hypercube to infinite (unbounded) space.
Abstract: Stochastic partition models divide a multi-dimensional space into a number of rectangular regions, such that the data within each region exhibit certain types of homogeneity. Due to the nature of their partition strategy, existing partition models may create many unnecessary divisions in sparse regions when trying to describe data in dense regions. To avoid this problem we introduce a new parsimonious partition model -- the Rectangular Bounding Process (RBP) -- to efficiently partition multi-dimensional spaces, by employing a bounding strategy to enclose data points within rectangular bounding boxes. Unlike existing approaches, the RBP possesses several attractive theoretical properties that make it a powerful nonparametric partition prior on a hypercube. In particular, the RBP is self-consistent and as such can be directly extended from a finite hypercube to infinite (unbounded) space. We apply the RBP to regression trees and relational models as a flexible partition prior. The experimental results validate the merit of the RBP {in rich yet parsimonious expressiveness} compared to the state-of-the-art methods.
11 citations
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TL;DR: In this paper, a numerical method was proposed to find the smallest boxes for bounding the feasible point sets defined by a nonlinear and nonconvex inequality and/or a system of non-linear and nonsmooth inequalities, unless the feasible set is convex.
11 citations