Results 1 to 10 of about 751,143 (344)
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 +5 more
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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 +4 more sources
Sign Problem in Tensor-Network Contraction [PDF]
PRX QuantumWe 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
Fermionic tensor network contraction for arbitrary geometries [PDF]
Physical Review ResearchWe describe our implementation of fermionic tensor network contraction on arbitrary lattices within both a globally ordered and a locally ordered formalism.
Yang Gao +7 more
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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
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
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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
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Fast counting with tensor networks [PDF]
SciPost Physics, 2019 We introduce tensor network contraction algorithms for counting satisfying assignments of constraint satisfaction problems (#CSPs). We represent each arbitrary #CSP formula as a tensor network, whose full contraction yields the number of satisfying ...
Stefanos Kourtis, Claudio Chamon, Eduardo R. Mucciolo, Andrei E. Ruckenstein
doaj +3 more sources
Optimizing Tensor Network Contraction Using Reinforcement Learning [PDF]
International Conference on Machine Learning, 2022 Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on classical computers.
Eli A. Meirom +3 more
openalex +3 more sources
Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction [PDF]
Quantum, 2022 We introduce a new open-source software library $Jet$, which uses task-based parallelism to obtain speed-ups in classical tensor-network simulations of quantum circuits.
Trevor Vincent +6 more
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