Results 1 to 10 of about 79,851 (290)
Hyperoptimized Approximate Contraction of Tensor Networks with Arbitrary Geometry [PDF]
Tensor network contraction is central to problems ranging from many-body physics to computer science. We describe how to approximate tensor network contraction through bond compression on arbitrary graphs.
Johnnie Gray, Garnet Kin-Lic Chan
doaj +4 more sources
qTorch: The quantum tensor contraction handler. [PDF]
Classical simulation of quantum computation is necessary for studying the numerical behavior of quantum algorithms, as there does not yet exist a large viable quantum computer on which to perform numerical tests.
E Schuyler Fried+5 more
doaj +6 more sources
Contraction Heuristics for Tensor Decision Diagrams [PDF]
In this paper, we study the equivalence problem for quantum circuits: Given two quantum circuits, are they equivalent? We reduce this problem to the contraction problem of a tensor network.
Christian Bøgh Larsen+3 more
doaj +4 more sources
Automatic contraction of unstructured tensor networks [PDF]
The evaluation of partition functions is a central problem in statistical physics. For lattice systems and other discrete models the partition function may be expressed as the contraction of a tensor network.
Adam S. Jermyn
doaj +6 more sources
Sign Problem in Tensor-Network Contraction
We investigate how the computational difficulty of contracting tensor networks depends on the sign structure of the tensor entries. Using results from computational complexity, we observe that the approximate contraction of tensor networks with only ...
Jielun Chen+3 more
doaj +6 more sources
Hyper-optimized tensor network contraction [PDF]
Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits.
Johnnie Gray, Stefanos Kourtis
doaj +6 more sources
On the Decomposition of Tensors by Contraction [PDF]
The decomposition of tensors into irreducible representations of the orthogonal groups is calculated for three and four dimensions. The connection is shown with the problem of the allowed values of ordinary and isotopic spin for a given symmetry of the spacial eigenfunction of a nuclear system.
Giulio Racah
openalex +3 more sources
Tensor networks contraction and the belief propagation algorithm [PDF]
Belief propagation is a well-studied message-passing algorithm that runs over graphical models and can be used for approximate inference and approximation of local marginals.
R. Alkabetz, I. Arad
doaj +5 more sources
On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond [PDF]
The contraction cost of a tensor network depends on the contraction order. However, the optimal contraction ordering problem is known to be NP-hard. We show that the linear contraction ordering problem for tree tensor networks admits a polynomial-time algorithm, by drawing connections to database join ordering.
Mihail Stoian+2 more
arxiv +3 more sources
Parameterization of tensor network contraction [PDF]
We present a conceptually clear and algorithmically useful framework for parameterizing the costs of tensor network contraction. Our framework is completely general, applying to tensor networks with arbitrary bond dimensions, open legs, and hyperedges.
arxiv +7 more sources