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ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more.
“Graphaf: a flow-based autoregressive model for molecular graph generation”
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2001
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“The theory and practice of simulated annealing”
Darrall Henderson, Sheldon H. Jacobson, and Alan W. Johnson · 2003
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“Improved simulation of stabilizer circuits”
Scott Aaronson and Daniel Gottesman · 2004
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“"Non-identity-check" is QMA-complete”
Dominik Janzing, Pawel Wocjan, and Thomas Beth · 2005
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“What matters for on-policy deep actor-critic methods? a large-scale study”
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, et al · 2006
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“ZX-calculus for the working quantum computer scientist” (2020)
John van de Wetering · 2012
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“A graphical approach to measurement-based quantum computing”
Ross Duncan · 2013
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“Playing atari with deep reinforcement learning” (2013)
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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“Reinforcement learning in robotics: A survey”
Jens Kober, J. Andrew Bagnell, and Jan Peters · 2013
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“Adam: A method for stochastic optimization” (2014)
Diederik P Kingma and Jimmy Ba · 2014
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“Picturing quantum processes: A first course in quantum theory and diagrammatic reasoning”
Bob Coecke and Aleks Kissinger · 2017
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“Proximal policy optimization algorithms” (2017)
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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“Neural message passing for quantum chemistry”
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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“A general reinforcement learning algorithm that masters chess, shogi, and go through self-play”
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018
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“Graph convolutional policy network for goal-directed molecular graph generation”
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
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“Reinforcement learning with neural networks for quantum feedback”
Thomas Fösel, Petru Tighineanu, Talitha Weiss, and Florian Marquardt · 2018
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“Reinforcement learning: An introduction”
Richard S Sutton and Andrew G Barto · 2018
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“Modular rl”
Schulmann John · 2018
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“A near-minimal axiomatisation of ZX-calculus for pure qubit quantum mechanics”
Renaud Vilmart · 2019
Cited alongside, same era.
“Graph-theoretic simplification of quantum circuits with the ZX-calculus”
Ross Duncan, Aleks Kissinger, Simon Perdrix, and John van de Wetering · 2020
Cited alongside, same era.
“Reducing the number of non-clifford gates in quantum circuits”
Aleks Kissinger and John van de Wetering · 2020
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“Tf-gnn: Graph neural networks in tensorflow” (2022)
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter Battaglia, Neslihan Bulut, et al · 2022
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“Graph neural networks: Foundations, frontiers, and applications”
Lingfei Wu, Peng Cui, Jian Pei, and Liang Zhao · 2022
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“Equivalence checking of quantum circuits with the ZX-calculus”
Tom Peham, Lukas Burgholzer, and Robert Wille · 2022
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“Barren plateaus in quantum tensor network optimization”
Enrique Cervero Martín, Kirill Plekhanov, and Michael Lubasch · 2023
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“Graphical structures for design and verification of quantum error correction”
Nicholas Chancellor, Aleks Kissinger, Stefan Zohren, Joschka Roffe, and Dominic Horsman · 2023
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“Learning dexterous in-hand manipulation”
Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2020
Cited alongside, same era.
“Reinforcement learning decoders for fault-tolerant quantum computation”
Ryan Sweke, Markus S Kesselring, Evert P L van Nieuwenburg, and Jens Eisert · 2020
Cited alongside, same era.
“Graph neural networks: A review of methods and applications”
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
Cited alongside, same era.
“Reducing T-count with the ZX-calculus”
Aleks Kissinger and John van de Wetering · 2020
Cited alongside, same era.
“Approximating KL divergence”
Schulmann John · 2020
Cited alongside, same era.
“Experimental deep reinforcement learning for error-robust gate-set design on a superconducting quantum computer”
Yuval Baum, Mirko Amico, Sean Howell, Michael Hush, Maggie Liuzzi, Pranav Mundada, Thomas Merkh, Andre R.R. Carvalho, and Michael J. Biercuk · 2021
Cited alongside, same era.
Tristan Cam and Simon Martiel · 2023
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“Annealing optimisation of mixed ZX phase circuits”
Stefano Gogioso and Richie Yeung · 2023
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David Winderl, Qunsheng Huang, and Christian B Mendl · 2023
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Jan Olle, Remmy Zen, Matteo Puviani, and Florian Marquardt · 2023
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“Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system”
Kevin Reuer, Jonas Landgraf, Thomas Fösel, James O’Sullivan, Liberto Beltrán, Abdulkadir Akin, Graham J Norris, Ants Remm, Michael Kerschbaum, Jean-Claude Besse, et al · 2023
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“Quarl: A learning-based quantum circuit optimizer” (2023)
Zikun Li, Jinjun Peng, Yixuan Mei, Sina Lin, Yi Wu, Oded Padon, and Zhihao Jia · 2023
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“Flow-preserving ZX-calculus rewrite rules for optimisation and obfuscation”
Tommy McElvanney and Miriam Backens · 2023
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“Equivalence checking of parameterized quantum circuits: Verifying the compilation of variational quantum algorithms”
Tom Peham, Lukas Burgholzer, and Robert Wille · 2023
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“Reinforcement learning based circuit compilation via ZX-calculus”
Jan Nogué Gómez · 2023
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“Code for optimizing ZX-diagrams with deep reinforcement learning”
Maximilian Nägele · 2023
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