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Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates to achieve the quantum advantage in solving combinatorial optimization problems.
2014
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2014
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J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,” Nature communications , vol. 9, no. 1, p. 4812, 2018
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R. Shaydulin, I. Safro, and J. Larson, “Multistart methods for quantum approximate optimization,” in 2019 IEEE high performance extreme computing conference (HPEC) . IEEE, 2019, pp. 1–8
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I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” Nature Physics , vol. 15, no. 12, pp. 1273–1278, 2019
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PmLR, 2021, pp. 8748–8763
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2022
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E. R. Anschuetz and B. T. Kiani, “Quantum variational algorithms are swamped with traps,” Nature Communications , vol. 13, no. 1, p. 7760, 2022
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A. Kulshrestha and I. Safro, “Beinit: Avoiding barren plateaus in variational quantum algorithms,” 2022
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S. Hadfield, Z. Wang, B. O’gorman, E. G. Rieffel, D. Venturelli, and R. Biswas, “From the quantum approximate optimization algorithm to a quantum alternating operator ansatz,” Algorithms , vol. 12, no. 2, p. 34, 2019
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2019
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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,” Physical Review X , vol. 10, no. 2, p. 021067, 2020
2020
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2020
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C. Outeiral, M. Strahm, J. Shi, G. M. Morris, S. C. Benjamin, and C. M. Deane, “The prospects of quantum computing in computational molecular biology,” Wiley Interdisciplinary Reviews: Computational Molecular Science , vol. 11, no. 1, p. e1481, 2021
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2022
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D. Herman, C. Googin, X. Liu, Y. Sun, A. Galda, I. Safro, M. Pistoia, and Y. Alexeev, “Quantum computing for finance,” Nature Reviews Physics , vol. 5, no. 8, pp. 450–465, 2023
2023
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2023
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2024
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2024
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J. Falla, Q. Langfitt, Y. Alexeev, and I. Safro, “Graph representation learning for parameter transferability in quantum approximate optimization algorithm,” Quantum Machine Intelligence , vol. 6, no. 2, 2024
2024
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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,” Quantum , vol. 8, p. 1231, 2024
2024
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2024
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2025
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