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Shallow Nitrogen-Vacancy (NV) centers are promising candidates for high-precision sensing applications; these defects, when positioned a few nanometers below the surface, provide an atomic-scale resolution along with substantial…

In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically, graph kernels are widely used in fields of bioinformatics, chemistry and financial data analysis. However, existing methods, especially entropy…

Machine Learning · Computer Science 2023-03-27 Chengyu Sun , Xing Ai , Zhihong Zhang , Edwin R Hancock

Entanglement entropy, which is a measure of quantum correlations between separate parts of a many-body system, has emerged recently as a fundamental quantity in broad areas of theoretical physics, from cosmology and field theory to…

Quantum Physics · Physics 2009-03-09 Israel Klich , Leonid Levitov

Designing the sensing architecture for large-scale spatio-temporal systems is hard when accuracy requirements are specified but sensor models are uncertain or unavailable. Classical design treats sensor placement and estimation…

Information Theory · Computer Science 2026-05-13 Raktim Bhattacharya

We compute the Renyi entropy in a one-dimensional transverse-field quantum Ising model by employing a swapping operator acting on the states which are prepared from the neural network methods. In the static ground state, Renyi entropy can…

Disordered Systems and Neural Networks · Physics 2024-03-15 Han-Qing Shi , Hai-Qing Zhang

During the past decade, interest has grown significantly in developing ultrasensitive widefield diamond magnetometry for various applications. Despite attempts to improve the adoption of conventional frame-based sensors, achieving high…

Instrumentation and Detectors · Physics 2023-06-27 Zhiyuan Du , Madhav Gupta , Feng Xu , Kai Zhang , Jiahua Zhang , Yan Zhou , Yiyao Liu , Zhenyu Wang , Jorg Wrachtrup , Ngai Wong , Can Li , Zhiqin Chu

Hybrid quantum devices expand the tools and techniques available for quantum sensing in various fields. Here, we experimentally demonstrate quantum sensing of the steady-state magnon population in a magnetostatic mode of a ferrimagnetic…

We calculate the quantum Renyi entropy in a phase space representation for either fermions or bosons. This can also be used to calculate purity and fidelity, or the entanglement between two systems. We show that it is possible to calculate…

Other Condensed Matter · Physics 2015-05-28 Laura E. C. Rosales-Zárate , P. D. Drummond

Background: Transitioning from an old medical coding system to a new one can be challenging, especially when the two coding systems are significantly different. The US experienced such a transition in 2015. Objective: This research aims to…

Information Theory · Computer Science 2020-12-24 Jerome Niyirora

While it is tempting in experimental practice to seek as high a data rate as possible, oversampling can become an issue if one takes measurements too densely. These effects can take many forms, some of which are easy to detect: e.g., when…

Information Theory · Computer Science 2021-03-03 Michael Neuder , Elizabeth Bradley , Edward Dlugokencky , James W. C. White , Joshua Garland

Nitrogen-vacancy (NV) spin ensembles in diamond provide an advanced magnetic sensing platform, with applications in both the physical and life sciences. The development of isotopically engineered $^{15}$NV diamond offers advantages over…

Electron Spin Resonance (ESR) is a widely common method in the field of quantum sensing. Specifically with the Nitrogen-Vacancy (NV) center in diamond, used for sensing magnetic and electric fields, strain and temperature. However, ESR…

Quantum Physics · Physics 2025-02-11 Galya Haim , Chris Mullarkey , John Howell , Nir Bar-Gill

We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, at least in low dimension, there…

Risk Management · Quantitative Finance 2015-06-23 Gregor Chliamovitch , Alexandre Dupuis , Bastien Chopard , Anton Golub

Entropy estimation is of practical importance in information theory and statistical science. Many existing entropy estimators suffer from fast growing estimation bias with respect to dimensionality, rendering them unsuitable for…

Information Theory · Computer Science 2023-08-22 Ziqiao Ao , Jinglai Li

Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of…

Information Theory · Computer Science 2015-11-24 Patricia Wollstadt , Mario Martínez-Zarzuela , Raul Vicente , Francisco J. Díaz-Pernas , Michael Wibral

Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of…

Quantum Physics · Physics 2024-07-10 S. Hernández-Gómez , F. Balducci , G. Fasiolo , P. Cappellaro , N. Fabbri , A. Scardicchio

A scheme is presented to extract detailed dynamical signatures from successive measurements of complex systems. Relative entropy based time series tools are used to quantify the gain in predictive power of increasing past knowledge. By…

Data Analysis, Statistics and Probability · Physics 2008-12-31 Nick S. Jones

Quantum metrology is a promising application of quantum technologies, enabling the precise measurement of weak external fields at a local scale. In typical quantum sensing protocols, a qubit interacts with an external field, and the…

Quantum Physics · Physics 2025-05-09 Hideaki Kawaguchi , Yuichiro Mori , Takahiko Satoh , Yuichiro Matsuzaki

We introduce a novel entropy-related function, \textit{non-repeatability}, designed to capture dynamical behaviors in complex systems. Its normalized form, \textit{mutability}, has been previously applied in statistical physics as a…

Statistical Mechanics · Physics 2025-04-04 Eugenio E. Vogel , Francisco J. Peña , G. Saravia , P. Vargas

Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable,…

Machine Learning · Computer Science 2021-06-08 Will Grathwohl , Jacob Kelly , Milad Hashemi , Mohammad Norouzi , Kevin Swersky , David Duvenaud
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