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We consider holographic entanglement entropy in higher derivative gravity theories. Recently Lewkowycz and Maldacena arXiv:1304.4926 have provided a method to derive the equations for the entangling surface from first principles. We use…

High Energy Physics - Theory · Physics 2015-06-16 Arpan Bhattacharyya , Apratim Kaviraj , Aninda Sinha

The celebrated holographic entanglement entropy triggered investigations on the connections between quantum information theory and quantum gravity. An important achievement is that we have gained more insights into the quantum states. It…

High Energy Physics - Theory · Physics 2022-07-15 Nan Li , Chuan-Shi Dong , Dong-Hui Du , Fu-Wen Shu

The Gradient Ascent Pulse Engineering (GRAPE) is a celebrated control algorithm with excellent converging rates, owing to a piece-wise-constant ansatz for the control function that allows for cheap objective gradients. However, the…

Quantum Physics · Physics 2021-02-17 Martin Larocca , Diego Wisniacki

Simulating non-wetting fluid invasion in volumetric images of porous materials is of broad interest in applications as diverse as electrochemical devices and CO2 sequestration. Among available methods, image-based algorithms offer much…

Fluid Dynamics · Physics 2025-01-07 Jeff T Gostick , Niloofar Misaghian , Ashkan Irannezhad , Benzhong Zhao

We discuss the relation between coarse-graining and the holographic principle in the framework of loop quantum gravity and ask the following question: when we coarse-grain arbitrary spin network states of quantum geometry, are we…

General Relativity and Quantum Cosmology · Physics 2018-08-29 Etera R. Livine

I give a short review of the holographic approach to quantum gravity, with emphasis on its application to deriving the properties of elementary particles.

High Energy Physics - Theory · Physics 2009-09-18 T. Banks

Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar…

Machine Learning · Computer Science 2024-04-16 Tianhao Peng , Wenjun Wu , Haitao Yuan , Zhifeng Bao , Zhao Pengrui , Xin Yu , Xuetao Lin , Yu Liang , Yanjun Pu

This dissertation reviews several recent advances at the intersection of quantum information and holography. In holography, properties of quantum systems admit a gravitational interpretation via the AdS/CFT correspondence. For holographic…

Quantum Physics · Physics 2024-06-05 William Munizzi

We investigate fundamental connections between thermodynamics and quantum information theory. First, we show that the operational framework of thermal operations is nonequivalent to the framework of Gibbs-preserving maps, and we comment on…

Quantum Physics · Physics 2016-07-13 Philippe Faist

We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of particle physics decays and the flexibility of message…

High Energy Physics - Phenomenology · Physics 2023-10-16 Lukas Ehrke , John Andrew Raine , Knut Zoch , Manuel Guth , Tobias Golling

We derive the generalized Gauss-Codazzi-Mainardi (GCM) equation for a general affine connection with torsion and non-metricity. Moreover, we show that the metric compatibility and torsionless condition of a connection on a manifold are…

General Relativity and Quantum Cosmology · Physics 2021-08-02 Seramika Ariwahjoedi , Agus Suroso , F. P. Zen

Some essential conceptual aspects that will fill some logical gaps of the frame to interpret the gravity as an entropic force was investigated, we focus on some crucial issues that didn't emphasized in Verlinde's original…

General Relativity and Quantum Cosmology · Physics 2014-02-21 Qiao-Jun Cao

We consider the controversial hypothesis that gravity is an entropic force that has its origin in the thermodynamics of holographic screens. Several key aspects of entropic gravity are discussed. In particular, we revisit and elaborate on…

High Energy Physics - Theory · Physics 2012-05-23 Masud Chaichian , Markku Oksanen , Anca Tureanu

Large scale numerical experiments are commonplace today in theoretical physics. The high performance algorithms described herein are the most compact, efficient methods known for representing and analyzing systems modeled well by sets or…

General Relativity and Quantum Cosmology · Physics 2018-05-14 William J. Cunningham

We propose a theory for coupling matter fields with discrete geometry on higher-order networks, i.e. cell complexes. The key idea of the approach is to associate to a higher-order network the quantum entropy of its metric. Specifically we…

Disordered Systems and Neural Networks · Physics 2024-09-12 Ginestra Bianconi

The issues of holography and possible links with gauge theories in spacetime physics is discussed, in an approach quite distinct from the more restricted AdS-CFT correspondence. A particular notion of holography in the context of black hole…

General Relativity and Quantum Cosmology · Physics 2009-08-20 Parthasarathi Majumdar

Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to…

Quantum Physics · Physics 2024-01-22 Xing Ai , Zhihong Zhang , Luzhe Sun , Junchi Yan , Edwin Hancock

We investigate theories in which gravity arises as a consequence of entropy. We distinguish between two approaches to this idea: holographic gravity, in which Einstein's equation arises from keeping entropy stationary in equilibrium under…

High Energy Physics - Theory · Physics 2016-06-23 Sean M. Carroll , Grant N. Remmen

Entropic measures like Shannon entropy (SE), its quantum mechanical analogue von Neumann entropy, and Kullback-Leibler divergence (KL) are key components in many tools used in physics, information theory, machine learning (ML) and quantum…

Machine Learning · Computer Science 2026-04-27 Illia Horenko , Davide Bassetti , Lukáš Pospíšil

The correlation matrix is a central representation of functional brain networks in neuroimaging. Traditional analyses often treat pairwise interactions independently in a Euclidean setting, overlooking the intrinsic geometry of correlation…

Machine Learning · Statistics 2025-04-10 Kisung You , Yelim Lee , Hae-Jeong Park