Results 1 to 10 of about 672,947 (320)

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   +8 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   +6 more sources

Using Ensemble Learning to Improve Automatic Vectorization of Tensor Contraction Program

IEEE Access, 2018
Automatic vectorization is crucial for improving the performance of computationally intensive programs. Existing compilers use conservative optimization strategies for automatic vectorization, which, in many cases, lead to the loss of vectorization ...
Hui Liu, Rongcai Zhao, Kai Nie
doaj   +3 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

GENERALIZED HARDI INVARIANTS BY METHOD OF TENSOR CONTRACTION. [PDF]

Proc IEEE Int Symp Biomed Imaging, 2014
Invariants play an important role in diffusion MRI (dMRI). They represent tissue properties, such as diffusion anisotropy, and are used for registration, tissue segmentation and classification, as well as white matter integrity measures in clinical studies of debilitating brain diseases.
Gur Y, Johnson CR.
europepmc   +5 more sources

Fermionic tensor network contraction for arbitrary geometries

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   +3 more sources

A Practical Guide to the Numerical Implementation of Tensor Networks I: Contractions, Decompositions, and Gauge Freedom [PDF]

Frontiers in Applied Mathematics and Statistics, 2022
We present an overview of the key ideas and skills necessary to begin implementing tensor network methods numerically, which is intended to facilitate the practical application of tensor network methods for researchers that are already versed with their ...
Glen Evenbly
doaj   +2 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   +2 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   +3 more sources