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Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers.
“Learning to learn with quantum neural networks via classical neural networks” (2019)
Guillaume Verdon, Michael Broughton, Jarrod R. McClean, Kevin J. Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, and Masoud Mohseni · 1907
Earlier work this paper cites.
“Reinforcement-learning-based variational quantum circuits optimization for combinatorial problems” (2019)
Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, and Prasanna Balaprakash · 1911
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“Portfolio rebalancing experiments using the quantum alternating operator ansatz” (2019)
Mark Hodson, Brendan Ruck, Hugh Ong, David Garvin, and Stefan Dulman · 1911
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“Real and complex analysis”
Walter Rudin · 1974
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“Principles of mathematical analysis”
Walter Rudin · 1976
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“A sequence of approximated solutions to the s-k model for spin glasses”
G Parisi · 1980
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“On the method of bounded differences”
Colin McDiarmid · 1989
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“Quantum optimization”
Tad Hogg and Dmitriy Portnov · 2000
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“Monte carlo methods in financial engineering”
Paul Glasserman · 2004
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“The Parisi formula”
Michel Talagrand · 2006
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“The BOBYQA algorithm for bound constrained optimization without derivatives”
Michael JD Powell · 2009
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“Quantum computation and quantum information”
Michael A Nielsen and Isaac L Chuang · 2010
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“The Sherrington-Kirkpatrick model”
Dmitry Panchenko · 2013
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“A quantum approximate optimization algorithm” (2014)
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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“On the method of typical bounded differences”
Lutz Warnke · 2016
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“Extremal cuts of sparse random graphs”
Amir Dembo, Andrea Montanari, and Subhabrata Sen · 2017
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“Performance of the quantum approximate optimization algorithm on the maximum cut problem” (2018)
Gavin E Crooks · 2018
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“For fixed control parameters the quantum approximate optimization algorithm’s objective function value concentrates for typical instances” (2018)
Fernando G. S. L. Brandão, Michael Broughton, Edward Farhi, Sam Gutmann, and Hartmut Neven · 2018
Earlier work this paper cites.
“Quantum approximate optimization algorithm for MaxCut: A fermionic view”
Zhihui Wang, Stuart Hadfield, Zhang Jiang, and Eleanor G. Rieffel · 2018
Earlier work this paper cites.
“Quantum algorithms for scientific computing and approximate optimization” (2018)
Stuart Hadfield · 2018
Earlier work this paper cites.
“High-dimensional probability: An introduction with applications in data science”
Roman Vershynin · 2018
Earlier work this paper cites.
“From the quantum approximate optimization algorithm to a quantum alternating operator ansatz”
Stuart Hadfield, Zhihui Wang, Bryan O’Gorman, Eleanor G Rieffel, Davide Venturelli, and Rupak Biswas · 2019
Cited alongside, same era.
“Multistart methods for quantum approximate optimization”
Ruslan Shaydulin, Ilya Safro, and Jeffrey Larson · 2019
Cited alongside, same era.
“Evaluating quantum approximate optimization algorithm: A case study”
Ruslan Shaydulin and Yuri Alexeev · 2019
Cited alongside, same era.
“Training the quantum approximate optimization algorithm without access to a quantum processing unit”
Michael Streif and Martin Leib · 2020
Cited alongside, same era.
“Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices”
Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, and Mikhail D. Lukin · 2020
Cited alongside, same era.
“The quantum approximate optimization algorithm at high depth for maxcut on large-girth regular graphs and the sherrington-kirkpatrick model” (2021)
Joao Basso, Edward Farhi, Kunal Marwaha, Benjamin Villalonga, and Leo Zhou · 2021
Later among the works it cites.
“Quantum walk-based portfolio optimisation”
N. Slate, E. Matwiejew, S. Marsh, and J. B. Wang · 2021
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“A survey of quantum computing for finance” (2022)
Dylan Herman, Cody Googin, Xiaoyuan Liu, Alexey Galda, Ilya Safro, Yue Sun, Marco Pistoia, and Yuri Alexeev · 2022
Later among the works it cites.
“Solving boolean satisfiability problems with the quantum approximate optimization algorithm” (2022)
Sami Boulebnane and Ashley Montanaro · 2022
Later among the works it cites.
“The quantum approximate optimization algorithm at high depth for maxcut on large-girth regular graphs and the sherrington-kirkpatrick model”
Joao Basso, Edward Farhi, Kunal Marwaha, Benjamin Villalonga, and Leo Zhou · 2022
Later among the works it cites.
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“Learning to optimize variational quantum circuits to solve combinatorial problems”
Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, and Prasanna Balaprakash · 2020
Cited alongside, same era.
“Reinforcement-learning-assisted quantum optimization”
Matteo M. Wauters, Emanuele Panizon, Glen B. Mbeng, and Giuseppe E. Santoro · 2020
Cited alongside, same era.
“Accelerating quantum approximate optimization algorithm using machine learning”
Mahabubul Alam, Abdullah Ash-Saki, and Swaroop Ghosh · 2020
Cited alongside, same era.
“A classical algorithm which also beats 1 2 + 2 π 1 d \frac{1}{2}+\frac{2}{\pi}\frac{1}{\sqrt{d}} for high girth max-cut” (2021)
Matthew B. Hastings · 2021
Cited alongside, same era.
“Predicting parameters for the quantum approximate optimization algorithm for max-cut from the infinite-size limit” (2021)
Sami Boulebnane and Ashley Montanaro · 2021
Cited alongside, same era.
“Parameters fixing strategy for quantum approximate optimization algorithm”
Xinwei Lee, Yoshiyuki Saito, Dongsheng Cai, and Nobuyoshi Asai · 2021
Cited alongside, same era.
“Quantum annealing initialization of the quantum approximate optimization algorithm”
Stefan H. Sack and Maksym Serbyn · 2021
Cited alongside, same era.
“Benchmarking the performance of portfolio optimization with qaoa”
Sebastian Brandhofer, Daniel Braun, Vanessa Dehn, Gerhard Hellstern, Matthias Hüls, Yanjun Ji, Ilia Polian, Amandeep Singh Bhatia, and Thomas Wellens · 2022
Later among the works it cites.
“The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size”
Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Leo Zhou · 2022
Later among the works it cites.
“Iterative-free quantum approximate optimization algorithm using neural networks” (2022)
Ohad Amosy, Tamuz Danzig, Ely Porat, Gal Chechik, and Adi Makmal · 2022
Later among the works it cites.
“Tensor network quantum simulator with step-dependent parallelization”
Danylo Lykov, Roman Schutski, Alexey Galda, Valeri Vinokur, and Yuri Alexeev · 2022
Later among the works it cites.
“Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models”
Joao Basso, David Gamarnik, Song Mei, and Leo Zhou · 2022
Later among the works it cites.
“Exploiting in-constraint energy in constrained variational quantum optimization”
Tianyi Hao, Ruslan Shaydulin, Marco Pistoia, and Jeffrey Larson · 2022
Later among the works it cites.
“The NLopt nonlinear-optimization package” (2022)
Steven G. Johnson · 2022
Later among the works it cites.
“Importance of kernel bandwidth in quantum machine learning”
Ruslan Shaydulin and Stefan M. Wild · 2022
Later among the works it cites.
“Bandwidth enables generalization in quantum kernel models” (2022)
Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan, Stefan M. Wild, and Ruslan Shaydulin · 2022
Later among the works it cites.
“Escaping from the barren plateau via gaussian initializations in deep variational quantum circuits”
Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, and Dacheng Tao · 2022
Later among the works it cites.
“Parameter transfer for quantum approximate optimization of weighted MaxCut”
Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson, James Ostrowski, and Travis S. Humble · 2023
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“Peptide conformational sampling using the quantum approximate optimization algorithm”
Sami Boulebnane, Xavier Lucas, Agnes Meyder, Stanislaw Adaszewski, and Ashley Montanaro · 2023
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“A depth-progressive initialization strategy for quantum approximate optimization algorithm”
Xinwei Lee, Ningyi Xie, Dongsheng Cai, Yoshiyuki Saito, and Nobuyoshi Asai · 2023
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“Constrained optimization via quantum zeno dynamics”
Dylan Herman, Ruslan Shaydulin, Yue Sun, Shouvanik Chakrabarti, Shaohan Hu, Pierre Minssen, Arthur Rattew, Romina Yalovetzky, and Marco Pistoia · 2023
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“Alignment between initial state and mixer improves qaoa performance for constrained optimization”
Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti, Dylan Herman, Changhao Li, Yue Sun, and Marco Pistoia · 2023
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