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The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers.
Classical and quantum bounded depth approximation algorithms
M. B. Hastings · 1905
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Binary pulse compression codes
A. Boehmer · 1967
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Synthesis of low-peak-factor signals and binary sequences with low autocorrelation (corresp.)
M. Schroeder · 1970
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A class of finite binary sequences with alternate auto-correlation values equal to zero (corresp.)
M. Golay · 1972
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Binary sequences up to length 40 with best possible autocorrelation function
J. Lindner · 1975
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Unified matrix treatment of the fast Walsh–Hadamard transform
Fino and Algazi · 1976
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Sieves for low autocorrelation binary sequences
M. Golay · 1977
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The merit factor of long low autocorrelation binary sequences (corresp.)
M. Golay · 1982
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Binary sequences with a maximally flat amplitude spectrum
GFM Beenker, TACM Claasen, and PWC Hermens · 1985
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Low autocorrelation binary sequences : statistical mechanics and configuration space analysis
J. Bernasconi · 1987
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A new search for skewsymmetric binary sequences with optimal merit factors
M.J.E. Golay and D.B. Harris · 1990
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A quantum algorithm for finding the minimum
Christoph Dürr and Peter Høyer · 1996
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Exhaustive search for low-autocorrelation binary sequences
S Mertens · 1996
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Tabu Search
Fred Glover and Manuel Laguna · 1997
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On the ground states of the Bernasconi model
Stephan Mertens and Christine Bessenrodt · 1998
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Tight bounds on quantum searching
Michel Boyer, Gilles Brassard, Peter Høyer, and Alain Tapp · 1998
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Quantum optimization
Tad Hogg and Dmitriy Portnov · 2000
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Landscape statistics of the low-autocorrelation binary string problem
Fernando F Ferreira, José F Fontanari, and Peter F Stadler · 2000
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Quantum amplitude amplification and estimation
Gilles Brassard, Peter Høyer, Michele Mosca, and Alain Tapp · 2002
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Reliable cost predictions for finding optimal solutions to labs problem: Evolutionary and alternative algorithms
Franc Brglez, Xiao Yu Li, Matthias F. Stallmann, and Burkhard Militzer · 2003
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A note on low autocorrelation binary sequences
Iván Dotú and Pascal Van Hentenryck · 2006
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A memetic algorithm for the low autocorrelation binary sequence problem
Jose E. Gallardo, Carlos Cotta, and Antonio J. Fernandez · 2007
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Quantum simulations of classical annealing processes
R. D. Somma, S. Boixo, H. Barnum, and E. Knill · 2008
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Speedup via quantum sampling
Pawel Wocjan and Anura Abeyesinghe · 2008
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Finding low autocorrelation binary sequences with memetic algorithms
José E. Gallardo, Carlos Cotta, and Antonio J. Fernández · 2009
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The BOBYQA algorithm for bound constrained optimization without derivatives
Michael JD Powell · 2009
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A mixed integer quadratic programming model for the low autocorrelation binary sequence problem
Jozef Kratica · 2012
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Advances in the merit factor problem for binary sequences
Jonathan Jedwab, Daniel J. Katz, and Kai-Uwe Schmidt · 2013
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Improved branch-and-bound for low autocorrelation binary sequences
S. D. Prestwich · 2013
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Fast quantum methods for optimization
S. Boixo, G. Ortiz, and R. Somma · 2015
Cited alongside, same era.
A GitHub Archive for Solvers and Solutions of the LABS
B. Bošković, F. Brglez, and J. Brest · 2016
Cited alongside, same era.
Low autocorrelation binary sequences
Tom Packebusch and Stephan Mertens · 2016
Cited alongside, same era.
Low-autocorrelation binary sequences: On improved merit factors and runtime predictions to achieve them
Borko Bošković, Franc Brglez, and Janez Brest · 2017
Cited alongside, same era.
Quantum-walk speedup of backtracking algorithms
Ashley Montanaro · 2018
Cited alongside, same era.
A short path quantum algorithm for exact optimization
M. B. Hastings · 2018
Cited alongside, same era.
Empirical performance bounds for quantum approximate optimization
Phillip C. Lotshaw, Travis S. Humble, Rebekah Herrman, James Ostrowski, and George Siopsis · 2021
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Classical symmetries and the quantum approximate optimization algorithm
Ruslan Shaydulin, Stuart Hadfield, Tad Hogg, and Ilya Safro · 2021
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Universal quantum speedup for branch-and-bound, branch-and-cut, and tree-search algorithms
Shouvanik Chakrabarti, Pierre Minssen, Romina Yalovetzky, and Marco Pistoia · 2022
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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
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Solving Boolean satisfiability problems with the quantum approximate optimization algorithm
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A heuristic algorithm for a low autocorrelation binary sequence problem with odd length and high merit factor
Janez Brest and Borko Boskovic · 2018
Cited alongside, same era.
Performance of the quantum approximate optimization algorithm on the maximum cut problem
Gavin E Crooks · 2018
Cited alongside, same era.
Quantum approximate optimization algorithm for MaxCut: A fermionic view
Zhihui Wang, Stuart Hadfield, Zhang Jiang, and Eleanor G. Rieffel · 2018
Cited alongside, same era.
Multistart methods for quantum approximate optimization
Ruslan Shaydulin, Ilya Safro, and Jeffrey Larson · 2019
Cited alongside, same era.
Quantum speedup of branch-and-bound algorithms
Ashley Montanaro · 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.
Sami Boulebnane and Ashley Montanaro · 2022
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Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer
Pradeep Niroula, Ruslan Shaydulin, Romina Yalovetzky, Pierre Minssen, Dylan Herman, Shaohan Hu, and Marco Pistoia · 2022
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Characterizing error mitigation by symmetry verification in QAOA
Ashish Kakkar, Jeffrey Larson, Alexey Galda, and Ruslan Shaydulin · 2022
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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
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Protecting expressive circuits with a quantum error detection code
Chris N. Self, Marcello Benedetti, and David Amaro · 2022
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Iterative-free quantum approximate optimization algorithm using neural networks
Ohad Amosy, Tamuz Danzig, Ely Porat, Gal Chechik, and Adi Makmal · 2022
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The NLopt nonlinear-optimization package, 2022
Steven G. Johnson · 2022
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Approximate Boltzmann distributions in quantum approximate optimization, 2022
Phillip C. Lotshaw, George Siopsis, James Ostrowski, Rebekah Herrman, Rizwanul Alam, Sarah Powers, and Travis S. Humble · 2022
Later among the works it cites.
Circuit depth scaling for quantum approximate optimization
V. Akshay, H. Philathong, E. Campos, D. Rabinovich, I. Zacharov, Xiao-Ming Zhang, and J. D. Biamonte · 2022
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Low-depth circuit implementation of parity constraints for quantum optimization
Josua Unger, Anette Messinger, Benjamin E. Niehoff, Michael Fellner, and Wolfgang Lechner · 2022
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Mind the gap: Achieving a super-grover quantum speedup by jumping to the end
Alexander M. Dalzell, Nicola Pancotti, Earl T. Campbell, and Fernando G.S.L. Brandão · 2023
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Parameter setting in quantum approximate optimization of weighted problems
Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, and Marco Pistoia · 2023
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Fast simulation of high-depth QAOA circuits
Danylo Lykov, Ruslan Shaydulin, Yue Sun, Yuri Alexeev, and Marco Pistoia · 2023
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A race track trapped-ion quantum processor
S. A. Moses et al · 2023
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Quantum error mitigation by Pauli check sandwiching
Alvin Gonzales, Ruslan Shaydulin, Zain H. Saleem, and Martin Suchara · 2023
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QAOA with N ⋅ p ≥ 200 N\cdot p\geq 200
Ruslan Shaydulin and Marco Pistoia · 2023
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Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti, Dylan Herman, Changhao Li, Yue Sun, and Marco Pistoia · 2023
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High-round QAOA for max k k -sat on trapped ion NISQ devices
Elijah Pelofske, Andreas Bärtschi, John Golden, and Stephan Eidenbenz · 2023
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Quantum annealing vs. QAOA: 127 qubit higher-order ising problems on NISQ computers
Elijah Pelofske, Andreas Bärtschi, and Stephan Eidenbenz · 2023
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Local algorithms and the failure of log-depth quantum advantage on sparse random csps
Antares Chen, Neng Huang, and Kunal Marwaha · 2023
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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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FrozenQubits: Boosting fidelity of QAOA by skipping hotspot nodes
Ramin Ayanzadeh, Narges Alavisamani, Poulami Das, and Moinuddin Qureshi · 2023
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Training the quantum approximate optimization algorithm without access to a quantum processing unit
Michael Streif and Martin Leib · 2058
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Evaluation of QAOA based on the approximation ratio of individual samples
Jason Larkin, Matías Jonsson, Daniel Justice, and Gian Giacomo Guerreschi · 2058
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t | | ket ⟩ {\rangle} : a retargetable compiler for NISQ devices
Seyon Sivarajah, Silas Dilkes, Alexander Cowtan, Will Simmons, Alec Edgington, and Ross Duncan · 2058
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