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The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time.
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2022
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2022
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2022
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R. Shaydulin, P. C. Lotshaw, J. Larson, J. Ostrowski, and T. S. Humble, “Parameter transfer for quantum approximate optimization of weighted maxcut,” ACM Trans. Quantum Comp. , 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3584706
2023
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2023
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Z. Zhou, Y. Du, X. Tian, and D. Tao, “QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines,” Physical Review Applied , vol. 19, no. 2, p. 024027, 2023
2023
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P. C. Lotshaw, H. Xu, B. Khalid, G. Buchs, T. S. Humble, and A. Banerjee, “Simulations of frustrated ising hamiltonians using quantum approximate optimization,” Philosophical Transactions of the Royal Society A , vol. 381, no. 2241, p. 20210414, 2023
2023
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P. Díez-Valle, D. Porras, and J. J. García-Ripoll, “Quantum approximate optimization algorithm pseudo-boltzmann states,” Phys. Rev. Lett. , vol. 130, p. 050601, Feb 2023. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.130.050601
2023
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