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. In particular, we introduce a hyper-optimization
Johnnie Gray, G. Chan
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Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction [PDF]
Quantum, 2021 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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Hyper-optimized tensor network contraction [PDF]
Quantum, 2020 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, S. Kourtis
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Treelike process tensor contraction for automated compression of environments [PDF]
Physical Review ResearchThe algorithm “automated compression of environments” (ACE) [M. Cygorek , ] provides a versatile way of simulating an extremely broad class of open quantum systems.
M. Cygorek +3 more
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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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Approximate Contraction of Arbitrary Tensor Networks with a Flexible and Efficient Density Matrix Algorithm [PDF]
QuantumTensor network contractions are widely used in statistical physics, quantum computing, and computer science. We introduce a method to efficiently approximate tensor network contractions using low-rank approximations, where each intermediate tensor ...
Linjian Ma +3 more
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Using Ensemble Learning to Improve Automatic Vectorization of Tensor Contraction Program
IEEE Access, 2018 Automatic vectorization is crucial for improving the performance of computationally intensive programs. Existing compilers use conservative optimization strategies for automatic vectorization, which, in many cases, lead to the loss of vectorization ...
Hui Liu, Rongcai Zhao, Keyu Nie
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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 Jermyn
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Diffusion Tensor Imaging of Skeletal Muscle Contraction Using Oscillating Gradient Spin Echo [PDF]
Frontiers in Neurology, 2021 Diffusion tensor imaging (DTI) measures water diffusion in skeletal muscle tissue and allows for muscle assessment in a broad range of neuromuscular diseases.
Valentina Mazzoli +4 more
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Contraction Heuristics for Tensor Decision Diagrams. [PDF]
Entropy (Basel)In this paper, we study the equivalence problem for quantum circuits: Given two quantum circuits, are they equivalent? We reduce this problem to the contraction problem of a tensor network.
Larsen CB +3 more
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