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The Quantum Approximate Optimization Algorithm (QAOA) is a promising quantum approach for tackling combinatorial optimization problems.
An algorithm for finding best matches in logarithmic expected time
Jerome H Friedman, Jon Louis Bentley, and Raphael Ari Finkel · 1977
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Rank-two relaxation heuristics for max-cut and other binary quadratic programs
Samuel Burer, Renato DC Monteiro, and Yin Zhang · 2002
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Randomized heuristics for the max-cut problem
Paola Festa, Panos M Pardalos, Mauricio GC Resende, and Celso C Ribeiro · 2002
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Multigrid solvers and multilevel optimization strategies
Achi Brandt and Dorit Ron · 2003
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Gset - a suite-style benchmark for graph processing systems
Yuan Ye · 2003
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A low-level hybridization between memetic algorithm and vns for the max-cut problem
Abraham Duarte, Angel Sanchez, Felipe Fernández, and Raúl Cabido · 2005
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Diversification-driven tabu search for unconstrained binary quadratic problems
Fred Glover, Zhipeng Lü, and Jin-Kao Hao · 2010
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Algebraic distance on graphs
Jie Chen and Ilya Safro · 2011
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The university of florida sparse matrix collection
Timothy A Davis and Yifan Hu · 2011
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Quantum supremacy through the quantum approximate optimization algorithm
Edward Farhi and Aram W Harrow · 2016
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What works best when? a systematic evaluation of heuristics for max-cut and qubo
Iain Dunning, Swati Gupta, and John Silberholz · 2018
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Qaoa for max-cut requires hundreds of qubits for quantum speed-up
Gian Giacomo Guerreschi and Anne Y Matsuura · 2019
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Quantum computing: progress and prospects
Mark Horowitz and Emily Grumbling · 2019
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Multistart methods for quantum approximate optimization
Ruslan Shaydulin, Ilya Safro, and Jeffrey Larson · 2019
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What limits the simulation of quantum computers?
Yiqing Zhou, E Miles Stoudenmire, and Xavier Waintal · 2020
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Multilevel combinatorial optimization across quantum architectures
Hayato Ushijima-Mwesigwa, Ruslan Shaydulin, Christian FA Negre, Susan M Mniszewski, Yuri Alexeev, and Ilya Safro · 2021
Cited alongside, same era.
Quantum circuit cutting with maximum-likelihood tomography
Michael A Perlin, Zain H Saleem, Martin Suchara, and James C Osborn · 2021
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Training variational quantum algorithms is np-hard
Lennart Bittel and Martin Kliesch · 2021
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Warm-starting quantum optimization
Daniel J Egger, Jakub Mareček, and Stefan Woerner · 2021
Cited alongside, same era.
Expectation values from the single-layer quantum approximate optimization algorithm on ising problems
Asier Ozaeta, Wim van Dam, and Peter L McMahon · 2022
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Large-scale quantum approximate optimization via divide-and-conquer
Junde Li, Mahabubul Alam, and Swaroop Ghosh · 2022
Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem
Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti, Matthew DeCross, Dylan Herman, Niraj Kumar, Jeffrey Larson, Danylo Lykov, Pierre Minssen, Yue Sun, et al · 2024
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Improving quantum approximate optimization by noise-directed adaptive remapping
Filip B Maciejewski, Jacob Biamonte, Stuart Hadfield, and Davide Venturelli · 2024
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End-to-end protocol for high-quality qaoa parameters with few shots
Tianyi Hao, Zichang He, Ruslan Shaydulin, Jeffrey Larson, and Marco Pistoia · 2024
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Approximate solutions of combinatorial problems via quantum relaxations
Bryce Fuller, Charles Hadfield, Jennifer R Glick, Takashi Imamichi, Toshinari Itoko, Richard J Thompson, Yang Jiao, Marna M Kagele, Adriana W Blom-Schieber, Rudy Raymond, et al · 2024
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Cited alongside, same era.
Hybrid quantum-classical multilevel approach for maximum cuts on graphs
Anthony Angone, Xiaoyuan Liu, Ruslan Shaydulin, and Ilya Safro · 2023
Cited alongside, same era.
Divide and conquer for combinatorial optimization and distributed quantum computation
Teague Tomesh, Zain H Saleem, Michael A Perlin, Pranav Gokhale, Martin Suchara, and Margaret Martonosi · 2023
Cited alongside, same era.
Investigating the effect of circuit cutting in qaoa for the maxcut problem on nisq devices
Marvin Bechtold, Johanna Barzen, Frank Leymann, Alexander Mandl, Julian Obst, Felix Truger, and Benjamin Weder · 2023
Cited alongside, same era.
Parameter transfer for quantum approximate optimization of weighted maxcut
Ruslan Shaydulin, Phillip C Lotshaw, Jeffrey Larson, James Ostrowski, and Travis S Humble · 2023
Cited alongside, same era.
Similarity-based parameter transferability in the quantum approximate optimization algorithm
Alexey Galda, Eesh Gupta, Jose Falla, Xiaoyuan Liu, Danylo Lykov, Yuri Alexeev, and Ilya Safro · 2023
Cited alongside, same era.
Design and execution of quantum circuits using tens of superconducting qubits and thousands of gates for dense ising optimization problems
Filip B Maciejewski, Stuart Hadfield, Benjamin Hall, Mark Hodson, Maxime Dupont, Bram Evert, James Sud, M Sohaib Alam, Zhihui Wang, Stephen Jeffrey, et al · 2024
Cited alongside, same era.
Bhuvanesh Sundar and Maxime Dupont · 2024
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Atithi Acharya, Romina Yalovetzky, Pierre Minssen, Shouvanik Chakrabarti, Ruslan Shaydulin, Rudy Raymond, Yue Sun, Dylan Herman, Ruben S Andrist, Grant Salton, et al · 2024
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Scaling up the quantum divide and conquer algorithm for combinatorial optimization
Ibrahim Cameron, Teague Tomesh, Zain Saleem, and Ilya Safro · 2024
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A review of barren plateaus in variational quantum computing
Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob Biamonte, Patrick J Coles, Lukasz Cincio, Jarrod R McClean, Zoë Holmes, and M Cerezo · 2024
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Trainability barriers in low-depth qaoa landscapes
Joel Rajakumar, John Golden, Andreas Bärtschi, and Stephan Eidenbenz · 2024
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Pygad: An intuitive genetic algorithm python library
Ahmed Fawzy Gad · 2024
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Optimization via quantum preconditioning
Maxime Dupont, Tina Oberoi, and Bhuvanesh Sundar · 2025
Closest in time.
Towards large-scale quantum optimization solvers with few qubits
Marco Sciorilli, Lucas Borges, Taylor L Patti, Diego García-Martín, Giancarlo Camilo, Anima Anandkumar, and Leandro Aolita · 2025
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Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms
Moises Ponce, Rebekah Herrman, Phillip C Lotshaw, Sarah Powers, George Siopsis, Travis Humble, and James Ostrowski · 2025
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Benchmarking quantum optimization for the maximum-cut problem on a superconducting quantum computer
Maxime Dupont, Bhuvanesh Sundar, Bram Evert, David E Bernal Neira, Zedong Peng, Stephen Jeffrey, and Mark J Hodson · 2025
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Kien X Nguyen, Bao Bach, and Ilya Safro · 2025
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