Results 41 to 50 of about 735,278 (318)
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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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.
E. Meirom +3 more
semanticscholar +1 more source
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 +5 more sources
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
Spectral asymptotics for contracted tensor ensembles
Electronic Journal of Probability, 2023 v3: updated to incorporate feedback from referees, including a shorter proof of Proposition 4.2; 31 pages, 7 ...
Au, Benson, Garza-Vargas, Jorge
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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
semanticscholar +1 more source
Algorithms for tensor network contraction ordering
Machine Learning: Science and Technology, 2020 Abstract Contracting tensor networks is often computationally demanding. Well-designed contraction sequences can dramatically reduce the contraction cost. We explore the performance of simulated annealing and genetic algorithms, two common discrete optimization techniques, to this ordering problem.
Schindler, Frank, Jermyn, Adam S
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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
semanticscholar +1 more source
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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Audiometric findings with voluntary tensor tympani contraction [PDF]
Journal of Otolaryngology - Head & Neck Surgery, 2017 Background Tensor tympani contraction may have a “signature” audiogram. This study demonstrates audiometric findings during voluntary tensor tympani contraction. Methods Five volunteers possessing the ability to voluntarily contract their tensor tympani muscles were identified and enrolled.
Brandon Wickens +2 more
openaire +3 more sources