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arXiv:1203.1503

Changing the topology of Tensor Networks

Stefan Handschuh

Published Mar 7, 2012• 10 citations
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Abstract

In many applications, it is needed to change the topology of a tensor network directly and without approximation. This work will introduce a general scheme that satisfies these needs. We will describe the procedure by two examples and show its efficiency in terms of memory consumption and speed in various numerical experiments. In general, we are going to provide an algorithm to add an edge to a tensor network as well as an algorithm to remove an edge unless the resulting network is a connected graph.

DOI
10.48550/arxiv.1203.1503

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In many applications, it is needed to change the topology of a tensor network directly and without approximation. This work will introduce a general scheme that satisfies these needs. We will describe the procedure by two examples and show its efficiency in terms of memory consumption and speed in various numerical experiments. In general, we are going to provide an algorithm to add an edge to a tensor network as well as an algorithm to remove an edge unless the resulting network is a connected graph.
In many applications that deal with high dimensional data, it is important to not store the high dimensional object itself, but its representation in a data sparse way. This aims to reduce the storage and computational complexity. There is a general scheme for representing tensors with the help of sums of elementary tensors, where the summation structure is defined by a graph/network. This scheme allows to generalize commonly used approaches in representing a large amount of numerical data (that can be interpreted as a high dimensional object) using sums of elementary tensors. The classification does not only distinguish between elementary tensors and non-elementary tensors, but also describes the number of terms that is needed to represent an object of the tensor space. This classification is referred to as tensor network (format). This work uses the tensor network based approach and describes non-linear block Gauss-Seidel methods (ALS and DMRG) in the context of the general tensor network framework. Another contribution of the thesis is the general conversion of different tensor formats. We are able to efficiently change the underlying graph topology of a given tensor representation while using the similarities (if present) of both the original and the desired structure. This is an important feature in case only minor structural changes are required. In all approximation cases involving iterative methods, it is crucial to find and use a proper initial guess. For linear iteration schemes, a good initial guess helps to decrease the number of iteration steps that are needed to reach a certain accuracy, but it does not change the approximation result. For non-linear iteration schemes, the approximation result may depend on the initial guess. This work introduces a method to successively create an initial guess that improves some approximation results. This algorithm is based on successive rank 1 increments for the r-term format. There are still open questions about how to find the optimal tensor format for a given general problem (e.g. storage, operations, etc.). For instance in the case where a physical background is given, it might be efficient to use this knowledge to create a good network structure. There is however, no guarantee that a better (with respect to the problem) representation structure does not exist.
We introduce a new coarse-graining algorithm, tensor network skeletonization, for the numerical computation of tensor networks. This approach utilizes a structure-preserving skeletonization procedure to remove short-range entanglements effectively at every scale. This approach is first presented in the setting of a two-dimensional (2D) statistical Ising model and is then extended to higher-dimensional tensor networks and disordered systems. When applied to the Euclidean path integral formulation, this approach also gives rise to new efficient representations of the ground states for 1D and 2D quantum Ising models.
Tensor network methods are taking a central role in modern quantum physics and beyond. They can provide an efficient approximation to certain classes of quantum states, and the associated graphical language makes it easy to describe and pictorially reason about quantum circuits, channels, protocols, open systems and more. Our goal is to explain tensor networks and some associated methods as quickly and as painlessly as possible. Beginning with the key definitions, the graphical tensor network language is presented through examples. We then provide an introduction to matrix product states. We conclude the tutorial with tensor contractions evaluating combinatorial counting problems. The first one counts the number of solutions for Boolean formulae, whereas the second is Penrose's tensor contraction algorithm, returning the number of 333-edge-colorings of 333-regular planar graphs.
In several applications including computational complexity and condensed matter physics, regular tensor networks arise in which vertex tensors and edge vector spaces are repeated. These have many internal edges, each with a label i∈{1,…,n}i\in\{1,\dots,n\}i∈{1,…,n} and corresponding to an tit_iti​-dimensional complex vector space. We have a local adjoint action on the vertex tensors by Gt=×i=1nGL(ti,C)G_{\textbf{t}}= \times_{i=1}^n{GL(t_i,\mathbb{C})}Gt​=×i=1n​GL(ti​,C) which amounts to a change of basis on each internal edge. Such networks are overparameterized in the sense that this action does not change the tensor represented by the entire network, although it may change the tensor associated to a given vertex. When GtG_{\textbf{t}}Gt​ acts nontrivially only on wires which are internal (perhaps after connecting copies of a network with dangling wires), the value of the network is of course a GtG_{\textbf{t}}Gt​ invariant. We explain how to use some tools from invariant theory to work modulo such an internal local GLGLGL symmetry and apply them to a problem in complexity theory.
Tensor networks provide extremely powerful tools for the study of complex classical and quantum many-body problems. Over the past two decades, the increment in the number of techniques and applications has been relentless, and especially the last ten years have seen an explosion of new ideas and results that may be overwhelming for the newcomer. This short review introduces the basic ideas, the best established methods, and some of the most significant algorithmic developments that are expanding the boundaries of the tensor network potential. The goal of this review is to help the reader not only appreciate the many possibilities offered by tensor networks but also find their way through state-of-the-art codes, their applicability, and some avenues of ongoing progress.
Tensor networks provide succinct representations of quantum many-body states and are an important computational tool for strongly correlated quantum systems. Their expressive and computational power is characterized by an underlying entanglement structure, on a lattice or more generally a (hyper)graph, with virtual entangled pairs or multipartite entangled states associated to (hyper)edges. Changing this underlying entanglement structure into another can lead to both theoretical and computational benefits. We study a natural resource theory which generalizes the notion of bond dimension to entanglement structures using multipartite entanglement. It is a direct extension of resource theories of tensors studied in the context of multipartite entanglement and algebraic complexity theory, allowing for the application of the sophisticated methods developed in these fields to tensor networks. The resource theory of tensor networks concerns both the local entanglement structure of a quantum many-body state and the (algebraic) complexity of tensor network contractions using this entanglement structure. We show that there are transformations between entanglement structures which go beyond edge-by-edge conversions, highlighting efficiency gains of our resource theory that mirror those obtained in the search for better matrix multiplication algorithms. We also provide obstructions to the existence of such transformations by extending a variety of methods originally developed in algebraic complexity theory for obtaining complexity lower bounds. The resource theory of tensor networks allows to compare different entanglement structures and should lead to more efficient tensor network representations and contraction algorithms.