Results 21 to 30 of about 79,851 (290)
Optimized contraction scheme for tensor-network states [PDF]
Phys. Rev. B 96, 045128 (2017), 2017 In the tensor-network framework, the expectation values of two-dimensional quantum states are evaluated by contracting a double-layer tensor network constructed from initial and final tensor-network states. The computational cost of carrying out this contraction is generally very high, which limits the largest bond dimension of tensor-network states ...
Rui-Zhen Huang +6 more
arxiv +5 more sources
On the Decomposition of Tensors by Contraction [PDF]
Proceedings of the National Academy of Sciences, 1952 F. D. Murnaghan
openalex +5 more sources
Treelike process tensor contraction for automated compression of environments [PDF]
Physical Review ResearchThe algorithm “automated compression of environments” (ACE) [M. Cygorek et al., Nat. Phys. 18, 662 (2022)1745-247310.1038/s41567-022-01544-9] provides a versatile way of simulating an extremely broad class of open quantum systems.
Moritz Cygorek +3 more
doaj +2 more sources
The Complexity of Contracting Planar Tensor Network [PDF]
arXiv, 2020 Tensor networks have been an important concept and technique in many research areas, such as quantum computation and machine learning. We study the exponential complexity of contracting tensor networks on two special graph structures: planar graphs and finite element graphs.
arxiv +3 more sources
Contraction of matchgate tensor networks on non-planar graphs [PDF]
Contemporary Mathematics, Vol. 482, pp. 179-211 (2009), 2008 A tensor network is a product of tensors associated with vertices of some graph $G$ such that every edge of $G$ represents a summation (contraction) over a matching pair of indexes. It was shown recently by Valiant, Cai, and Choudhary that tensor networks can be efficiently contracted on planar graphs if components of every tensor obey a system of ...
Sergey Bravyi
arxiv +3 more sources
TensorTrace: an application to contract tensor networks [PDF]
arXiv, 2019 Tensor network methods are a conceptually elegant framework for encoding complicated datasets, where high-order tensors are approximated as networks of low-order tensors. In practice, however, the numeric implementation of tensor network algorithms is often a labor-intensive and error-prone task, even for experienced researchers in this area.
arxiv +3 more sources
Contractions: Nijenhuis and Saletan tensors for general algebraic structures [PDF]
Journal of Physics A: Mathematical and General, 2001 Generalizations in many directions of the contraction procedure for Lie algebras introduced by E.J.Saletan are proposed. Products of arbitrary nature, not necessarily Lie brackets, are considered on sections of finite-dimensional vector bundles.
José F. Cariñena +2 more
openalex +5 more sources
Analytical cache modeling and tilesize optimization for tensor contractions [PDF]
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2019 Data movement between processor and memory hierarchyis a fundamental bottleneck that limits the performance ofmany applications on modern computer architectures. Tilingand loop permutation are key techniques for improving datalocality. However, selecting effective tile-sizes and loop permutations is particularly challenging for tensor contractions ...
Rui Li +6 more
openalex +5 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
doaj +1 more source
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
doaj +1 more source