Results 11 to 20 of about 751,143 (344)
Contraction Heuristics for Tensor Decision Diagrams [PDF]
EntropyIn 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
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Polyhedral Specification and Code Generation of Sparse Tensor Contraction with Co-Iteration [PDF]
ACM Transactions on Architecture and Code Optimization (TACO), 2022 This article presents a code generator for sparse tensor contraction computations. It leverages a mathematical representation of loop nest computations in the sparse polyhedral framework (SPF), which extends the polyhedral model to support non-affine ...
Tuowen Zhao +4 more
openalex +3 more sources
Efficient parallelization of tensor network contraction for simulating quantum computation [PDF]
Nature Computational Science, 2021 We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach.
Cupjin Huang +20 more
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Treelike process tensor contraction for automated compression of environments [PDF]
Physical Review ResearchThe algorithm “automated compression of environments” (ACE) [M. Cygorek et al., Nat. Phys. 18, 662 (2022)1745-247310.1038/s41567-022-01544-9] provides a versatile way of simulating an extremely broad class of open quantum systems.
Moritz Cygorek +3 more
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Tensor Contraction Layers for Parsimonious Deep Nets [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Tensors offer a natural representation for many kinds of data frequently encountered in machine learning. Images, for example, are naturally represented as third order tensors, where the modes correspond to height, width, and channels. Tensor methods are
Anandkumar, Anima +4 more
core +4 more sources
Parameterization of Tensor Network Contraction [PDF]
, 2019 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 ...
O\u27Gorman, Bryan
core +5 more sources
Lecture Notes of Tensor Network Contractions [PDF]
, 2019 Tensor network (TN), a young mathematical tool of high vitality and great potential, has been undergoing extremely rapid developments in the last two decades, gaining tremendous success in condensed matter physics, atomic physics, quantum information ...
Chen, Xi +6 more
core +8 more sources
Tensor networks contraction and the belief propagation algorithm [PDF]
Physical Review Research, 2021 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 +3 more sources
Diffusion Tensor Imaging of Skeletal Muscle Contraction Using Oscillating Gradient Spin Echo [PDF]
Frontiers in Neurology, 2021 Diffusion tensor imaging (DTI) measures water diffusion in skeletal muscle tissue and allows for muscle assessment in a broad range of neuromuscular diseases.
Valentina Mazzoli +4 more
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Strassen's Algorithm for Tensor Contraction [PDF]
SIAM Journal on Scientific Computing, 2017 Tensor contraction (TC) is an important computational kernel widely used in numerous applications. It is a multidimensional generalization of matrix multiplication (GEMM).
Jianyu Huang, D. Matthews, R. Geijn
semanticscholar +5 more sources