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A promising new algebraic approach to weighted model counting makes use of tensor networks, following a reduction from weighted model counting to tensor-network contraction.
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Glen Evenbly and Robert NC Pfeifer · 2014
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Jean-Marie Lagniez and Pierre Marquis · 2014
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A practical introduction to tensor networks: Matrix product states and projected entangled pair states
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Robert Pfeifer, Jutho Haegeman, and Frank Verstraete · 2014
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Classical simulation of intermediate-size quantum circuits
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Benchmarking treewidth as a practical component of tensor network simulations
Eugene F Dumitrescu, Allison L Fisher, Timothy D Goodrich, Travis S Humble, Blair D Sullivan, and Andrew L Wright · 2018
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Weighted model counting on the GPU by exploiting small treewidth
Johannes K Fichte, Markus Hecher, Stefan Woltran, and Markus Zisser · 2018
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Graph bisection with pareto optimization
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Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions
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Jinsung Kim, Aravind Sukumaran-Rajam, Vineeth Thumma, Sriram Krishnamoorthy, Ajay Panyala, Louis-Noël Pouchet, Atanas Rountev, and P Sadayappan · 2019
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Chase Roberts, Ashley Milsted, Martin Ganahl, Adam Zalcman, Bruce Fontaine, Yijian Zou, Jack Hidary, Guifre Vidal, and Stefan Leichenauer · 2019
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A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware
Benjamin Villalonga, Sergio Boixo, Bron Nelson, Christopher Henze, Eleanor Rieffel, Rupak Biswas, and Salvatore Mandrà · 2019
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ADDMC: Weighted model counting with algebraic decision diagrams
Jeffrey M Dudek, Vu H N Phan, and Moshe Y Vardi · 2020
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