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The ZX-calculus is an algebraic formalism that allows quantum computations to be simplified via a small number of simple graphical rewrite rules.
Cambridge University Press, 10.1017/CBO9780511976667
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Cambridge University Press, 10.1017/9781316219317
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In: Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science
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Kang Feng Ng & Quanlong Wang (2018): Completeness of the ZX-calculus for Pure Qubit Clifford+T Quantum Mechanics · 2018
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Ph.D. thesis, Wolfson College, University of Oxford
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In Bob Coecke & Matthew Leifer, editors: Proceedings 16th International Conference on Quantum Physics and Logic, Chapman University, Orange, CA, USA., 10-14 June 2019
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In Bob Coecke & Matthew Leifer, editors: Proceedings 16th International Conference on Quantum Physics and Logic,
Aleks Kissinger & John van de Wetering (2020): PyZX: Large Scale Automated Diagrammatic Reasoning · 2019
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In Steven T. Flammia, editor: 15th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2020)
Niel de Beaudrap, Xiaoning Bian & Quanlong Wang (2020): Fast and Effective Techniques for T-Count Reduction via Spider Nest Identities · 2020
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NVIDIA, Péter Vingelmann & Frank H.P. Fitzek (2020): CUDA, release: 10.2.89 · 2020
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In Hakjoo Oh, editor: Programming Languages and Systems
Agustín Borgna, Simon Perdrix & Benoît Valiron (2021): Hybrid quantum-classical circuit simplification with the ZX-calculus · 2021
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Master’s thesis, University of Oxford
Julien Codsi (2022): Cutting-Edge Graphical Stabiliser Decompositions for Classical Simulation of Quantum Circuits · 2022
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arXiv preprint arXiv:2212.08609
Julien Codsi & John van de Wetering (2022): Classically Simulating Quantum Supremacy IQP Circuits trough a Random Graph Approach · 2022
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In Shane Mansfield, Benoit Valîron & Vladimir Zamdzhiev, editors: Proceedings of the Twentieth International Conference on Quantum Physics and Logic, Paris, France, 17-21st July 2023
Tommy McElvanney & Miriam Backens (2023): Flow-preserving ZX-calculus Rewrite Rules for Optimisation and Obfuscation · 2023
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arXiv preprint arXiv:2311.18588
Maximilian Nägele & Florian Marquardt (2023): Optimizing ZX-Diagrams with Deep Reinforcement Learning · 2023
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Master’s thesis, University of Oxford
Wira Azmoon Ahmad (2024): Efficient Heuristics for Classical Simulation of Quantum Circuits Using ZX-Calculus · 2024
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arXiv preprint arXiv:2412.17182
Wira Azmoon Ahmad & Matthew Sutcliffe (2024): Dynamic T-decomposition for classical simulation of quantum circuits · 2024
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In: The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24
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In Stefano Gogioso & Matty Hoban, editors: Proceedings 19th International Conference on Quantum Physics and Logic, Wolfson College, Oxford, UK, 27 June - 1 July 2022
Stefano Gogioso & Richie Yeung (2023): Annealing Optimisation of Mixed ZX Phase Circuits · 2022
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In François Le Gall & Tomoyuki Morimae, editors: 17th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2022)
Aleks Kissinger, John van de Wetering & Renaud Vilmart (2022): Classical Simulation of Quantum Circuits with Partial and Graphical Stabiliser Decompositions · 2022
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In: Proceedings of the 59th ACM/IEEE Design Automation Conference
Robert Wille, Lukas Burgholzer, Stefan Hillmich, Thomas Grurl, Alexander Ploier & Tom Peham (2022): The Basis of Design Tools for Quantum Computing: Arrays, Decision Diagrams, Tensor Networks, and ZX-Calculus · 2022
Cited alongside, same era.
arXiv preprint arXiv:2305.02669
Tristan Cam & Simon Martiel (2023): Speeding up quantum circuits simulation using ZX-Calculus · 2023
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arXiv preprint arXiv:2312.02793
Calum Holker (2023): Causal flow preserving optimisation of quantum circuits in the ZX-calculus · 2023
Cited alongside, same era.
arXiv preprint arXiv:2307.01803
Mark Koch, Richie Yeung & Quanlong Wang (2023): Speedy Contraction of ZX Diagrams with Triangles via Stabiliser Decompositions · 2023
Cited alongside, same era.
Available at https://github.com/Quantomatic/pyzx
Aleks Kissinger & John van de Wetering: PyZX
Cited in the paper.
Alexander Koziell-Pipe, Richie Yeung & Matthew Sutcliffe (2024): Towards Faster Quantum Circuit Simulation Using Graph Decompositions, GNNs and Reinforcement Learning · 2024
Closest in time.
arXiv preprint arXiv:2409.00828
Matthew Sutcliffe (2024): Smarter k-Partitioning of ZX-Diagrams for Improved Quantum Circuit Simulation · 2024
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Electronic Proceedings in Theoretical Computer Science
Matthew Sutcliffe & Aleks Kissinger (2024): Procedurally Optimised ZX-Diagram Cutting for Efficient T-Decomposition in Classical Simulation · 2024
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[Preprint]
Matthew Sutcliffe (2025): Classically simulating quantum circuits with ZX-calculus · 2025
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Ph.D. thesis, University of Oxford, Oxford, United Kingdom
Matthew Sutcliffe (2025): Novel Methods for Classical Simulation of Quantum Circuits via ZX-Calculus · 2025
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Quantum Science and Technology
Aleks Kissinger & John van de Wetering (2022): Simulating quantum circuits with ZX-calculus reduced stabiliser decompositions · 2058
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