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The Quantum Approximate Optimization Algorithm (QAOA) is a variational ansatz that resembles the Trotterized dynamics of a Quantum Annealing (QA) protocol.
L.-A. Wu, M. S. Byrd, and D. A. Lidar, Polynomial-time simulation of pairing models on a quantum computer,
2002
Earlier work this paper cites.
D. Aharonov, W. van Dam, J. Kempe, Z. Landau, S. Lloyd, and O. Regev, Adiabatic quantum computation is equivalent to standard quantum computation, in
2004
Earlier work this paper cites.
D. A. Lidar, Towards fault tolerant adiabatic quantum computation,
2008
Earlier work this paper cites.
G. Kochenberger, J.-K. Hao, F. Glover, M. Lewis, Z. Lü, H. Wang, and Y. Wang, The unconstrained binary quadratic programming problem: A survey,
2014
Earlier work this paper cites.
T. Albash and D. A. Lidar, Adiabatic quantum computation,
2018
Earlier work this paper cites.
G. Pagano, A. Bapat, P. Becker, K. S. Collins, A. De, P. W. Hess, H. B. Kaplan, A. Kyprianidis, W. L. Tan, C. Baldwin, L. T. Brady, A. Deshpande, F. Liu, S. Jordan, A. V. Gorshkov, and C. Monroe, Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator,
2020
Earlier work this paper cites.
L. Zhou, S.-T. Wang, S. Choi, H. Pichler, and M. D. Lukin, Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices,
2020
Earlier work this paper cites.
P. Virtanen
2020
Earlier work this paper cites.
Y. Susa and H. Nishimori, Variational optimization of the quantum annealing schedule for the Lechner-Hauke-Zoller scheme,
2021
Earlier work this paper cites.
D. J. Egger, J. Mareček, and S. Woerner, Warm-starting quantum optimization,
2021
Earlier work this paper cites.
L. Bittel and M. Kliesch, Training variational quantum algorithms is NP-hard,
2021
Earlier work this paper cites.
V. Akshay, D. Rabinovich, E. Campos, and J. Biamonte, Parameter concentrations in quantum approximate optimization,
2021
Earlier work this paper cites.
J. Claes and W. van Dam, Instance independence of single layer quantum approximate optimization algorithm on mixed-spin models at infinite size,
2021
Cited alongside, same era.
A. Galda, X. Liu, D. Lykov, Y. Alexeev, and I. Safro, Transferability of optimal QAOA parameters between random graphs, in
2021
Cited alongside, same era.
Y.-Q. Chen, Y. Chen, C.-K. Lee, S. Zhang, and C.-Y. Hsieh, Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks,
2022
Cited alongside, same era.
A. Deshpande and A. Melnikov, Capturing symmetries of quantum optimization algorithms using graph neural networks, Symmetry
2022
Cited alongside, same era.
J. Basso, E. Farhi, K. Marwaha, B. Villalonga, and L. Zhou, The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model, in
J. R. Finžgar, M. J. A. Schuetz, J. K. Brubaker, H. Nishimori, and H. G. Katzgraber, Designing quantum annealing schedules using bayesian optimization,
2024
Later among the works it cites.
E. Pelofske, A. Bärtschi, L. Cincio, J. Golden, and S. Eidenbenz, Scaling whole-chip QAOA for higher-order Ising spin glass models on heavy-hex graphs, npj Quantum Information
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
L. Cheng, Y.-Q. Chen, S.-X. Zhang, and S. Zhang, Quantum approximate optimization via learning-based adaptive optimization,
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2022
Cited alongside, same era.
E. Pelofske, A. Bärtschi, and S. Eidenbenz, Quantum annealing vs. QAOA: 127 Qubit Higher-Order Ising problems on NISQ computers, in
2023
Cited alongside, same era.
X. Lee, N. Xie, D. Cai, Y. Saito, and N. Asai, A depth-progressive initialization strategy for quantum approximate optimization algorithm, Mathematics
2023
Cited alongside, same era.
R. Shaydulin, P. C. Lotshaw, J. Larson, J. Ostrowski, and T. S. Humble, Parameter transfer for quantum approximate optimization of weighted MaxCut, ACM Transactions on Quantum Computing
2023
Cited alongside, same era.
G. Acampora, A. Chiatto, and A. Vitiello, Genetic algorithms as classical optimizer for the quantum approximate optimization algorithm,
2023
Cited alongside, same era.
P. Díez-Valle, D. Porras, and J. J. García-Ripoll, Quantum approximate optimization algorithm pseudo-boltzmann states,
2023
Cited alongside, same era.
P. C. Lotshaw, G. Siopsis, J. Ostrowski, R. Herrman, R. Alam, S. Powers, and T. S. Humble, Approximate Boltzmann distributions in quantum approximate optimization,
2023
Cited alongside, same era.
E. Farhi, J. Goldstone, and S. Gutmann,
Cited in the paper.
2024
Later among the works it cites.
P. Díez-Valle, D. Porras, and J. J. García-Ripoll, Connection between single-layer quantum approximate optimization algorithm interferometry and thermal distribution sampling,
2024
Later among the works it cites.
R. Shaydulin, C. Li, S. Chakrabarti, M. DeCross, D. Herman, N. Kumar, J. Larson, D. Lykov, P. Minssen, Y. Sun, Y. Alexeev, J. M. Dreiling, J. P. Gaebler, T. M. Gatterman, J. A. Gerber, K. Gilmore, D. Gresh, N. Hewitt, C. V. Horst, S. Hu, J. Johansen, M. Matheny, T. Mengle, M. Mills, S. A. Moses, B. Neyenhuis, P. Siegfried, R. Yalovetzky, and M. Pistoia, Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem,
2024
Later among the works it cites.
S. H. Sureshbabu, D. Herman, R. Shaydulin, J. Basso, S. Chakrabarti, Y. Sun, and M. Pistoia, Parameter setting in quantum approximate optimization of weighted problems,
2024
Later among the works it cites.
2024
Later among the works it cites.
I. Lyngfelt and L. García-Álvarez, Symmetry-informed transferability of optimal parameters in the quantum approximate optimization algorithm,
2025
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S. Boulebnane, J. Sud, R. Shaydulin, and M. Pistoia,
2025
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