Results 1 to 10 of about 666,598 (288)
qTorch: The quantum tensor contraction handler. [PDF]
PLoS One, 2018 Classical simulation of quantum computation is necessary for studying the numerical behavior of quantum algorithms, as there does not yet exist a large viable quantum computer on which to perform numerical tests.
Fried ES +5 more
europepmc +6 more sources
Fermionic tensor network contraction for arbitrary geometries [PDF]
Physical Review ResearchWe describe our implementation of fermionic tensor network contraction on arbitrary lattices within both a globally ordered and a locally ordered formalism.
Yang Gao +7 more
semanticscholar +6 more sources
Sign Problem in Tensor-Network Contraction [PDF]
PRX QuantumWe investigate how the computational difficulty of contracting tensor networks depends on the sign structure of the tensor entries. Using results from computational complexity, we observe that the approximate contraction of tensor networks with only ...
Jielun Chen +3 more
semanticscholar +3 more sources
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 +3 more sources
Polyhedral Specification and Code Generation of Sparse Tensor Contraction with Co-iteration [PDF]
ACM Transactions on Architecture and Code Optimization (TACO), 2022 This article presents a code generator for sparse tensor contraction computations. It leverages a mathematical representation of loop nest computations in the sparse polyhedral framework (SPF), which extends the polyhedral model to support non-affine ...
Tuowen Zhao +4 more
semanticscholar +3 more sources
Efficient parallelization of tensor network contraction for simulating quantum computation [PDF]
Nature Computational Science, 2021 We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach.
Cupjin Huang +20 more
semanticscholar +2 more sources
Computing vibrational energy levels using a canonical polyadic tensor method with a fixed rank and a contraction tree. [PDF]
Journal of Chemical Physics, 2023 In this paper, we use the previously introduced Canonical Polyadic (CP)-Multiple Shift Block Inverse Iteration (MSBII) eigensolver [S. D. Kallullathil and T. Carrington, J. Chem. Phys. 155, 234105 (2021)] in conjunction with a contraction tree to compute
Sangeeth Das Kallullathil, T. Carrington
semanticscholar +2 more sources
IEICE Trans. Inf. Syst., 2016 Modern optimizing compilers tend to be conservative and often fail to vectorize programs that would have benefited from it. In this paper, we propose a way to predict the relevant command-line options of the compiler so that it chooses the most ...
Antoine Trouvé +5 more
semanticscholar +3 more sources
Open Quantum System Dynamics from Infinite Tensor Network Contraction. [PDF]
Physical Review Letters, 2023 Approaching the long-time dynamics of non-Markovian open quantum systems presents a challenging task if the bath is strongly coupled. Recent proposals address this problem through a representation of the so-called process tensor in terms of a tensor ...
Valentin Link, Hong-Hao Tu, W. Strunz
semanticscholar +1 more source
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
semanticscholar +1 more source