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In these lecture notes, we present a pedagogical review of a number of related {\it numerically exact} approaches to quantum many-body problems. In particular, we focus on methods based on the exact diagonalization of the Hamiltonian matrix…

Strongly Correlated Electrons · Physics 2007-05-23 Reinhard M. Noack , Salvatore R. Manmana

In this article we present analytical results on the exact tensor network representations and correlation functions of the first examples of 2D ground states with quantum phase transitions between area law and extensive entanglement…

Quantum Physics · Physics 2025-03-26 Olai B. Mykland , Zhao Zhang

We propose a tensor-network algorithm for discrete-time stochastic dynamics of a homogeneous system in the thermodynamic limit. We map a $d$-dimensional nonequilibrium Markov process to a $(d+1)$-dimensional infinite tensor network by using…

Statistical Mechanics · Physics 2016-06-29 Yoshihito Hotta

We introduce a method for extracting meaningful entanglement measures of tensor network states in general dimensions. Current methods require the explicit reconstruction of the density matrix, which is highly demanding, or the contraction…

Strongly Correlated Electrons · Physics 2022-07-28 Noa Feldman , Augustine Kshetrimayum , Jens Eisert , Moshe Goldstein

We reconstruct entanglement thermodynamics by means of Hessian geometry, since this method exactly generalizes thermodynamics into much wider exponential family cases including quantum entanglement. Starting with the correct first law of…

High Energy Physics - Theory · Physics 2015-08-12 Hiroaki Matsueda

In this paper, we consider the network latency estimation, which has been an important metric for network performance. However, a large scale of network latency estimation requires a lot of computing time. Therefore, we propose a new method…

Networking and Internet Architecture · Computer Science 2023-07-14 Jun Lei , Ji-Qian Zhao , Jing-Qi Wang , An-Bao Xu

The mapping between gate voltages applied to a double quantum dot, and the parameters of a Hubbard-like Hamiltonian, is of utmost importance for understanding and operating spin qubits. State-of-the-art techniques for measuring Hamiltonian…

Network structures are reconstructed from dynamical data by respectively naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) approximations. For TAP approximation, we use two methods to reconstruct the network: a) iteration method; b)…

Computation · Statistics 2015-05-20 Hong-Li Zeng , Erik Aurell , Mikko Alava , Hamed Mahmoudi

We give a new proof for the area law for general 1D gapped systems, which exponentially improves Hastings' famous result \cite{ref:Has07}. Specifically, we show that for a chain of d-dimensional spins, governed by a 1D local Hamiltonian…

Quantum Physics · Physics 2013-01-08 Itai Arad , Alexei Kitaev , Zeph Landau , Umesh Vazirani

We devise a quantum-circuit algorithm to solve the ground state and ground energy of artificial graphene. The algorithm implements a Trotterized adiabatic evolution from a purely tight-binding Hamiltonian to one including kinetic,…

Generalized network tomography (GNT) deals with estimation of link performance parameters for networks with arbitrary topologies using only end-to-end path measurements of pure unicast probe packets. In this paper, by taking advantage of…

Statistics Theory · Mathematics 2012-10-31 Gugan Thoppe

The algebraic Bethe Ansatz is a prosperous and well-established method for solving one-dimensional quantum models exactly. The solution of the complex eigenvalue problem is thereby reduced to the solution of a set of algebraic equations.…

Strongly Correlated Electrons · Physics 2012-07-23 Valentin Murg , Vladimir E. Korepin , Frank Verstraete

The use of near-term quantum devices that lack quantum error correction, for addressing quantum chemistry and physics problems, requires hybrid quantum-classical algorithms and techniques. Here we present a process for obtaining the…

Quantum Physics · Physics 2023-08-14 Pejman Jouzdani , Stefan Bringuier

We study discounted Hamilton Jacobi equations on networks, without putting any restriction on their geometry. Assuming the Hamiltonians continuous and coercive, we establish a comparison principle and provide representation formulae for…

Analysis of PDEs · Mathematics 2025-09-09 Marco Pozza , Antonio Siconolfi

Tensor network states form a variational ansatz class widely used, both analytically and numerically, in the study of quantum many-body systems. It is known that if the underlying graph contains a cycle, e.g. as in projected entangled pair…

Quantum Physics · Physics 2021-05-26 Matthias Christandl , Fulvio Gesmundo , Daniel Stilck Franca , Albert H. Werner

Formulating non-Abelian gauge theories as a tensor network is known to be challenging due to the internal degrees of freedom that result in the degeneracy in the singular value spectrum. In two dimensions, it is straightforward to 'trace…

High Energy Physics - Theory · Physics 2024-07-17 Atis Yosprakob

Thermalization and its breakdown in interacting quantum many-body systems are governed by mid-spectrum eigenstates, which are typically accessible only in small system sizes amenable to exact diagonalization. Here we demonstrate that the…

Strongly Correlated Electrons · Physics 2026-03-31 Andrew Hallam , Jared Jeyaretnam , Zlatko Papić

Hamiltonian truncation is a non-perturbative numerical method for calculating observables of a quantum field theory. The starting point for this method is to truncate the interacting Hamiltonian to a finite-dimensional space of states…

High Energy Physics - Theory · Physics 2022-08-10 Timothy Cohen , Kara Farnsworth , Rachel Houtz , Markus A. Luty

Symmetry protected topological (SPT) phases with gapless edge excitations have been shown to exist in principle in strongly interacting bosonic/fermionic systems and it is highly desirable to find practical systems to realize such phases…

Strongly Correlated Electrons · Physics 2013-11-27 Ching-Yu Huang , Xie Chen , Feng-Li Lin

We study the Riemannian geometry of the Deep Linear Network (DLN) as a foundation for a thermodynamic description of the learning process. The main tools are the use of group actions to analyze overparametrization and the use of Riemannian…

Machine Learning · Computer Science 2026-05-22 Govind Menon , Tianmin Yu