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We investigate $(k_1,k_2)$-extendibility of fermionic Gaussian states, a property central to quantum correlations and approximations of separability. We show that these states are $(k_1,k_2)$-extendible if and only if they admit a fermionic…

Quantum Physics · Physics 2025-08-27 Amir-Reza Negari , Farzin Salek

In the tensor network representation, a deformed $Z_{2}$ topological ground state wave function is proposed and its norm can be exactly mapped to the two-dimensional solvable Ashkin-Teller (AT) model. Then the topological (toric code) phase…

Strongly Correlated Electrons · Physics 2019-05-08 Guo-Yi Zhu , Guang-Ming Zhang

Tensor networks are often used to accurately represent ground states of quantum spin chains. Two popular choices of such tensor network representations can be seen to implement linear maps that correspond, respectively, to euclidean time…

Strongly Correlated Electrons · Physics 2018-06-01 Ashley Milsted , Guifre Vidal

Studying finite-temperature properties with tensor networks is notoriously difficult, especially at low temperatures, due to the rapid growth of entanglement and the complexity of thermal states. Existing methods like purification and…

Strongly Correlated Electrons · Physics 2026-01-15 Tomoya Takahashi , Wei-Lin Tu , Ji-Yao Chen , Yusuke Nomura

Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Motivated by the desire to efficiently simulate many-body…

Strongly Correlated Electrons · Physics 2022-05-13 Di Luo , Giuseppe Carleo , Bryan K. Clark , James Stokes

A variational inference-based framework for training a multi-output Gaussian process latent variable model, specifically tailored to the tails-up spatio-temporal stream network, is developed. Training, given a censored observational data…

Methodology · Statistics 2026-05-21 Marno Basson , Tobias M. Louw , Theresa R. Smith

In this paper, we aim to find the conditions for input-state stability (ISS) and incremental input-state stability ($\delta$ISS) of Gated Graph Neural Networks (GGNNs). We show that this recurrent version of Graph Neural Networks (GNNs) can…

Robotics · Computer Science 2024-03-12 Antonio Marino , Claudio Pacchierotti , Paolo Robuffo Giordano

Recent works on quantum resource theories of non-Gaussianity, which are based upon the type of tools available in contemporary experimental settings, put Gaussian states and their convex combinations on equal footing. Motivated by this, in…

Quantum Physics · Physics 2022-11-14 Tomasz Linowski , Alexander Teretenkov , Łukasz Rudnicki

We present a study of the entanglement properties of Gaussian cluster states, proposed as a universal resource for continuous-variable quantum computing. A central aim is to compare mathematically-idealized cluster states defined using…

Quantum Physics · Physics 2010-11-30 Hugo Cable , Daniel E. Browne

In this work we propose a series-expansion thermal tensor network (SETTN) approach for efficient simulations of quantum lattice models. This continuous-time SETTN method is based on the numerically exact Taylor series expansion of…

Strongly Correlated Electrons · Physics 2017-04-12 Bin-Bin Chen , Yun-Jing Liu , Ziyu Chen , Wei Li

These notes are a short introduction to the mathematical theory of open quantum systems. They are meant to serve as an entry point into a broad research area which has applications across the quantum sciences dealing with systems subjected…

Quantum Physics · Physics 2026-03-13 Marco Merkli , Ángel Neira

We establish asymptotically Gaussian fluctuations for functionals of a large class of spin models and strongly correlated random point fields, achieving near-optimal rates. For spin models, we demonstrate Gaussian asymptotics for the…

Probability · Mathematics 2025-09-16 Tien-Cuong Dinh , Subhroshekhar Ghosh , Hoang-Son Tran , Manh-Hung Tran

A simplified relativistic kinetic theory for gases with internal degrees of freedom, based on a BGK-type collision term, is considered. First the Boltzmann equation is rewritten in tetrad form and then thermal coefficients are determined to…

General Relativity and Quantum Cosmology · Physics 2026-04-13 Philip Semrén , Michael Bradley , João M. S. Oliveira , M. Piedade Machado Ramos

A simple and efficient method for characterization of multidimensional Gaussian states is suggested and experimentally demonstrated. Our scheme shows analogies with tomography of finite dimensional quantum states, with the covariance matrix…

A phantom network of Gaussian chains far from the point of gelation can be described as a gas of interacting particles represented by the cross links. The type of particles varies with the network functionality, whereas the type of…

Soft Condensed Matter · Physics 2007-05-23 Benjamin Paul , Michael Schulz , Harry L. Frisch

On the lattice, a renormalization group (RG) flow for two-dimensional partition functions expressed as a tensor network can be obtained using the tensor network renormalization (TNR) algorithm [G. Evenbly, G. Vidal, Phys. Rev. Lett. 115…

High Energy Physics - Theory · Physics 2018-09-17 Qi Hu , Adrian Franco-Rubio , Guifre Vidal

Gaussian wave packets (GWPs) are well suited as basis functions to describe the time evolution of arbitrary wave functions in systems with nonsingular smooth potentials. They are less so in atomic systems on account of the singular behavior…

Atomic Physics · Physics 2015-05-13 Tomaž Fabčič , Jörg Main , Günter Wunner

In various approaches to quantum gravity continuum spacetime is expected to emerge from discrete geometries through a phase transition. In group field theory, various indications for such a transition have recently been found but a complete…

General Relativity and Quantum Cosmology · Physics 2018-12-14 Andreas G. A. Pithis , Johannes Thürigen

We discuss a one-parameter family of states in two-dimensional holographic conformal field theories which are constructed via the Euclidean path integral of an effective theory on a family of hyperbolic slices in the dual bulk geometry. The…

High Energy Physics - Theory · Physics 2021-05-19 Pawel Caputa , Jorrit Kruthoff , Onkar Parrikar

Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research problem. In…

Machine Learning · Computer Science 2013-11-12 Ying Liu , Alan S. Willsky