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The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve combinatorial optimization problems in near-term gate-based noisy quantum devices.
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J. Romero, J. P. Olson, and A. Aspuru-Guzik, “Quantum autoencoders for efficient compression of quantum data,” Quantum Science and Technology , vol. 2, no. 4, p. 045001, 2017
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2017
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S. Hadfield et al. , “Quantum approximate optimization with hard and soft constraints,” in Proceedings of the Second International Workshop on Post Moores Era Supercomputing . ACM, 2017, pp. 15–21
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2018
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2018
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2018
Later among the works it cites.
2018
Later among the works it cites.
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P.-L. Dallaire-Demers and N. Killoran, “Quantum generative adversarial networks,” Physical Review A , vol. 98, no. 1, p. 012324, 2018
2018
Cited alongside, same era.
I. B. M. Corporation. Ibm q experience. [Online]. Available: https://quantumexperience.ng.bluemix.net/qx/editor
Cited in the paper.
A. Ash-Saki, M. Alam, and S. Ghosh, “Qure: Qubit re-allocation in noisy intermediate-scale quantum computers,” in Proceedings of the 56th Annual Design Automation Conference 2019 . ACM, 2019, p. 141
2019
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