Results 1 to 10 of about 737,908 (337)

qTorch: The quantum tensor contraction handler. [PDF]

PLoS ONE, 2018
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, Nicolas P D Sawaya, Yudong Cao, Ian D Kivlichan, Jhonathan Romero, Alán Aspuru-Guzik   +5 more
doaj   +9 more sources

Sign Problem in Tensor-Network Contraction [PDF]

PRX Quantum
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, Jiaqing Jiang, Dominik Hangleiter, Norbert Schuch   +3 more
doaj   +7 more sources

Fermionic tensor network contraction for arbitrary geometries [PDF]

Physical Review Research
We describe our implementation of fermionic tensor network contraction on arbitrary lattices within both a globally ordered and a locally ordered formalism.
Yang Gao, Huanchen Zhai, Johnnie Gray, Ruojing Peng, Gunhee Park, Wen-Yuan Liu, Eirik F. Kjønstad, Garnet Kin-Lic Chan   +7 more
doaj   +7 more sources

Hyperoptimized Approximate Contraction of Tensor Networks with Arbitrary Geometry [PDF]

Physical Review X, 2022
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

Hyper-optimized tensor network contraction [PDF]

Quantum, 2021
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   +7 more sources

Faster identification of optimal contraction sequences for tensor networks [PDF]

Physical Review E, 2014
The efficient evaluation of tensor expressions involving sums over multiple indices is of significant importance to many fields of research, including quantum many-body physics, loop quantum gravity, and quantum chemistry.
Haegeman, Jutho, Pfeifer, Robert N. C., Verstraete, Frank   +2 more
core   +9 more sources

Contraction Heuristics for Tensor Decision Diagrams [PDF]

Entropy
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, Simon Brun Olsen, Kim Guldstrand Larsen, Christian Schilling   +3 more
doaj   +5 more sources

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, Furlanello, Tommaso, Khanna, Aran, Kossaifi, Jean, Lipton, Zachary C.   +4 more
core   +8 more sources

Automatic contraction of unstructured tensor networks [PDF]

SciPost Physics, 2020
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

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, Devin A. Matthews, Robert A. Geijn   +2 more
semanticscholar   +5 more sources