Author: Stefan DepenbrockPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Tensor Networks for the Simulation of Strongly Correlated Systems
Author: Stefan DepenbrockTensor Network Contractions
Author: Shi-Ju RanPublisher: Springer Nature
ISBN: 3030344894
Category : Science
Languages : en
Pages : 160
Book Description
Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from the numerical renormalization group approach proposed by K. G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum many-body physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.
Introduction to Tensor Network Methods
Author: Simone MontangeroPublisher: Springer
ISBN: 3030014096
Category : Science
Languages : en
Pages : 172
Book Description
This volume of lecture notes briefly introduces the basic concepts needed in any computational physics course: software and hardware, programming skills, linear algebra, and differential calculus. It then presents more advanced numerical methods to tackle the quantum many-body problem: it reviews the numerical renormalization group and then focuses on tensor network methods, from basic concepts to gauge invariant ones. Finally, in the last part, the author presents some applications of tensor network methods to equilibrium and out-of-equilibrium correlated quantum matter. The book can be used for a graduate computational physics course. After successfully completing such a course, a student should be able to write a tensor network program and can begin to explore the physics of many-body quantum systems. The book can also serve as a reference for researchers working or starting out in the field.
Tensor Network Techniques for Strongly Correlated Systems
Author: Benedikt BruognoloModelling Non-Markovian Quantum Systems Using Tensor Networks
Author: Aidan StrathearnPublisher: Springer Nature
ISBN: 3030549755
Category : Science
Languages : en
Pages : 113
Book Description
This thesis presents a revolutionary technique for modelling the dynamics of a quantum system that is strongly coupled to its immediate environment. This is a challenging but timely problem. In particular it is relevant for modelling decoherence in devices such as quantum information processors, and how quantum information moves between spatially separated parts of a quantum system. The key feature of this work is a novel way to represent the dynamics of general open quantum systems as tensor networks, a result which has connections with the Feynman operator calculus and process tensor approaches to quantum mechanics. The tensor network methodology developed here has proven to be extremely powerful: For many situations it may be the most efficient way of calculating open quantum dynamics. This work is abounds with new ideas and invention, and is likely to have a very significant impact on future generations of physicists.
Tensor Network Algorithms for Three-dimensional Quantum Systems
Author: Patrick VlaarPublisher:
ISBN: 9789493330313
Category :
Languages : en
Pages : 0
Book Description
"Strongly correlated systems can give rise to many types of fascinating emergent behavior, such as superconductivity or exotic magnetic phases. Numerical approaches have become essential tools to further our understanding of these systems. An important family is formed by algorithms based on tensor networks. In recent decades, these methods have turned into vital tools to study one- and two-dimensional quantum systems. Extensions of these algorithms to three-dimensional systems, though, have been relatively unexplored. The goal of this thesis is to develop new algorithms to study three-dimensional quantum systems. We make use of a tensor network Ansatz called the infinite projected entangled-pair state (iPEPS), which allows us to directly probe the thermodynamic limit. The main technical challenge is to find ways to evaluate expectation values, which require a contraction of the tensor network. In this thesis, we develop several efficient contraction algorithms both for general three-dimensional quantum systems and for layered two-dimensional quantum systems with weak interlayer coupling. We apply these algorithms to study the Shastry-Sutherland model, which closely describes the layered compound SrCu2(BO3)2. A discrepancy exists, however, in the extent of the plaquette phase, which is significantly smaller in the compound compared to the model. Through our simulations, we find that a possible explanation could be the interlayer coupling, which strongly reduces the extent of the plaquette phase already at weak coupling. With this thesis, we hope to show the potential of tensor networks for the accurate study of three-dimensional strongly-correlated quantum systems."--
Tensor Network Contractions
Author: Maciej LewensteinPublisher:
ISBN: 9781013273636
Category : Science
Languages : en
Pages : 158
Book Description
Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from the numerical renormalization group approach proposed by K. G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum many-body physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Connecting the Dots: Tensor Network Algorithms for Two-Dimensional Strongly-Correlated Systems
Author: Juan Camilo Osorio IreguiPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Tensor Network States and Effective Particles for Low-Dimensional Quantum Spin Systems
Author: Laurens VanderstraetenPublisher: Springer
ISBN: 9783319877457
Category : Science
Languages : en
Pages : 0
Book Description
This thesis develops new techniques for simulating the low-energy behaviour of quantum spin systems in one and two dimensions. Combining these developments, it subsequently uses the formalism of tensor network states to derive an effective particle description for one- and two-dimensional spin systems that exhibit strong quantum correlations. These techniques arise from the combination of two themes in many-particle physics: (i) the concept of quasiparticles as the effective low-energy degrees of freedom in a condensed-matter system, and (ii) entanglement as the characteristic feature for describing quantum phases of matter. Whereas the former gave rise to the use of effective field theories for understanding many-particle systems, the latter led to the development of tensor network states as a description of the entanglement distribution in quantum low-energy states.
Neural-Network Simulation of Strongly Correlated Quantum Systems
Author: Stefanie CzischekPublisher: Springer Nature
ISBN: 3030527158
Category : Science
Languages : en
Pages : 205
Book Description
Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.