Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell [PDF]
Quantum, 2020 Tensor network states provide an efficient class of states that faithfully capture strongly correlated quantum models and systems in classical statistical mechanics.
Alexander Nietner +4 more
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Stack Operation of Tensor Networks
Frontiers in Physics, 2022 The tensor network, as a factorization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction, and stacking.
Tianning Zhang +4 more
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Optimization Methods for Generalized Tensor Contraction
Bulletin of the South Ural State University Series Computational Mathematics and Software Engineering, 2020 Roman A. Gareev
semanticscholar +3 more sources
Athena: high-performance sparse tensor contraction sequence on heterogeneous memory
International Conference on Supercomputing, 2021 Sparse tensor contraction sequence has been widely employed in many fields, such as chemistry and physics. However, how to efficiently implement the sequence faces multiple challenges, such as redundant computations and memory operations, massive memory ...
Jiawen Liu +3 more
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Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation [PDF]
arXiv.org, 2020 Tensor networks are the main building blocks in a wide variety of computational sciences, ranging from many-body theory and quantum computing to probability and machine learning. Here we propose a parallel algorithm for the contraction of tensor networks
R. Schutski +3 more
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Constructing Optimal Contraction Trees for Tensor Network Quantum Circuit Simulation [PDF]
IEEE Conference on High Performance Extreme Computing, 2022 One of the key problems in tensor network based quantum circuit simulation is the construction of a contraction tree which minimizes the cost of the simulation, where the cost can be expressed in the number of operations as a proxy for the simulation ...
Cameron Ibrahim +4 more
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Simulating Quantum Circuits Using Efficient Tensor Network Contraction Algorithms with Subexponential Upper Bound. [PDF]
Physical Review Letters, 2022 We derive a rigorous upper bound on the classical computation time of finite-ranged tensor network contractions in d≥2 dimensions. Consequently, we show that quantum circuits of single-qubit and finite-ranged two-qubit gates can be classically simulated ...
T. Wahl, Sergii Strelchuk
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Verifying Quantum Advantage Experiments with Multiple Amplitude Tensor Network Contraction. [PDF]
Physical Review Letters, 2022 The quantum supremacy experiment, such as Google Sycamore [F. Arute et al., Nature (London) 574, 505 (2019).NATUAS0028-083610.1038/s41586-019-1666-5], poses a great challenge for classical verification due to the exponentially increasing compute cost ...
Yong Liu +13 more
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EURASIP Journal on Advances in Signal Processing, 2022 The slice-wise multiplication of two tensors is required in a variety of tensor decompositions (including PARAFAC2 and PARATUCK2) and is encountered in many applications, including the analysis of multidimensional biomedical data (EEG, MEG, etc.) or ...
Kristina Naskovska +3 more
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Tensor networks contraction and the belief propagation algorithm
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
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