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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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Minimum Cost Loop Nests for Contraction of a Sparse Tensor with a Tensor Network [PDF]
ACM Symposium on Parallelism in Algorithms and Architectures, 2023 Sparse tensor decomposition and completion are common in numerous applications, ranging from machine learning to computational quantum chemistry. Typically, the main bottleneck in optimization of these models are contractions of a single large sparse ...
Raghavendra Kanakagiri, Edgar Solomonik
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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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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
doaj +1 more source
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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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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High Voltage, 2023 In order to achieve high performance for solving the ion flow field problem, an approach is proposed with the tensor‐structured finite element method (FEM) to accelerate the Newton iteration.
Qiwen Cheng, Jun Zou
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Calculating contracted tensor Feynman integrals [PDF]
Physics Letters B, 2011 A recently derived approach to the tensor reduction of 5-point one-loop Feynman integrals expresses the tensor coefficients by scalar 1-point to 4-point Feynman integrals completely algebraically. In this letter we derive extremely compact algebraic expressions for the contractions of the tensor integrals with external momenta.
Fleischer, Jochem, Riemann, Tord
openaire +4 more sources
Fast counting with tensor networks
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
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