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Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth.
H. Markowitz, Portfolio selection, The Journal of Finance 7
1952
Earlier work this paper cites.
E. Lieb, T. Schultz, and D. Mattis, Two soluble models of an antiferromagnetic chain, Annals of Physics 16
1961
Earlier work this paper cites.
T. Hogg, Quantum search heuristics, Phys. Rev. A 61
2000
Earlier work this paper cites.
E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, Quantum computation by adiabatic evolution, Preprint at https://arxiv.org/abs/quant-ph/0001106 (2000)
2000
Earlier work this paper cites.
E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren, and D. Preda, A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem, Science 292
2001
Earlier work this paper cites.
T. Hogg, Adiabatic quantum computing for random satisfiability problems, Phys. Rev. A 67
2003
Earlier work this paper cites.
F. Verstraete, J. I. Cirac, and J. I. Latorre, Quantum circuits for strongly correlated quantum systems, Phys. Rev. A 79
2009
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. S. Otterbach et al. , Unsupervised machine learning on a hybrid quantum computer (2017)
2017
Earlier work this paper cites.
T. Albash and D. A. Lidar, Adiabatic quantum computation, Reviews of Modern Physics 90
2018
Earlier work this paper cites.
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 12
2019
Earlier work this paper cites.
M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Sci. Technol. 4
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Tranter, P. J. Love, F. Mintert, N. Wiebe, and P. V. Coveney, Ordering of trotterization: Impact on errors in quantum simulation of electronic structure, Entropy 21
2019
Earlier work this paper cites.
Z. Wang, N. C. Rubin, J. M. Dominy, and E. G. Rieffel, XY mixers: Analytical and numerical results for the quantum alternating operator ansatz, Phys. Rev. A 101
2020
Earlier work this paper cites.
R. Tate, M. Farhadi, C. Herold, G. Mohler, and S. Gupta, Bridging classical and quantum with SDP initialized warm-starts for QAOA, ACM Journal of the ACM (JACM) (2020)
2020
Earlier work this paper cites.
J. Cook, S. Eidenbenz, and A. Bärtschi, The quantum alternating operator ansatz on maximum k-vertex cover, in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, 2020) pp. 83–92
2020
Earlier work this paper cites.
J. Yao, M. Bukov, and L. Lin, Policy gradient based quantum approximate optimization algorithm, in Mathematical and Scientific Machine Learning (2020) pp. 605–634
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, Phys. Rev. X 10
2020
Earlier work this paper cites.
P. Virtanen et al. , Scipy 1.0: fundamental algorithms for scientific computing in python, Nature Methods 17
2020
Earlier work this paper cites.
S. Sivarajah, S. Dilkes, A. Cowtan, W. Simmons, A. Edgington, and R. Duncan, t | | ket⟩: a retargetable compiler for NISQ devices, Quantum Sci. Technol. 6
2020
Earlier work this paper cites.
R. Shaydulin, K. Marwaha, J. Wurtz, and P. C. Lotshaw, QAOAKit: A toolkit for reproducible study, application, and verification of QAOA, in Second International Workshop on Quantum Computing Software (2021)
2021
Earlier work this paper cites.
M. P. Harrigan et al. , Quantum approximate optimization of non-planar graph problems on a planar superconducting processor, Nature Physics 17
2021
Cited alongside, same era.
2021
Cited alongside, same era.
D. J. Egger, J. Mareček, and S. Woerner, Warm-starting quantum optimization, Quantum 5
2021
Cited alongside, same era.
N. Slate, E. Matwiejew, S. Marsh, and J. Wang, Quantum walk-based portfolio optimisation, Quantum 5
2021
Cited alongside, same era.
S. H. Sack and M. Serbyn, Quantum annealing initialization of the quantum approximate optimization algorithm, Quantum 5
2021
Cited alongside, same era.
Y. Chai, Y.-J. Han, Y.-C. Wu, Y. Li, M. Dou, and G.-P. Guo, Shortcuts to the quantum approximate optimization algorithm, Phys. Rev. A 105
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Gulania, Z. He, B. Peng, N. Govind, and Y. Alexeev, QuYBE-an algebraic compiler for quantum circuit compression, in 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) (2022) pp. 406–410
2022
Later among the works it cites.
S. Aktar, A. Bärtschi, A.-H. A. Badawy, and S. Eidenbenz, A divide-and-conquer approach to dicke state preparation, IEEE Transactions on Quantum Engineering 3
2022
Later among the works it cites.
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A. M. Childs, Y. Su, M. C. Tran, N. Wiebe, and S. Zhu, Theory of Trotter error with commutator scaling, Phys. Rev. X 11
2021
Cited alongside, same era.
R. Shaydulin and A. Galda, Error mitigation for deep quantum optimization circuits by leveraging problem symmetries, in 2021 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, 2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
S. Mandrà, J. Marshall, E. G. Rieffel, and R. Biswas, HybridQ: A hybrid simulator for quantum circuits, in 2021 IEEE/ACM Second International Workshop on Quantum Computing Software (QCS) (2021) pp. 99–109
2021
Cited alongside, same era.
C. Yi, Success of digital adiabatic simulation with large Trotter step, Phys. Rev. A 104
2021
Cited alongside, same era.
2022
Cited alongside, same era.
T. Tomesh, Z. H. Saleem, and M. Suchara, Quantum local search with the quantum alternating operator ansatz, Quantum 6
2022
Cited alongside, same era.
2022
Later among the works it cites.
F. G. Fuchs, K. O. Lye, H. Møll Nilsen, A. J. Stasik, and G. Sartor, Constraint preserving mixers for the quantum approximate optimization algorithm, Algorithms 15
2022
Later among the works it cites.
D. Lykov, R. Schutski, A. Galda, V. Vinokur, and Y. Alexeev, Tensor network quantum simulator with step-dependent parallelization, in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (2022) pp. 582–593
2022
Later among the works it cites.
C. Ibrahim, D. Lykov, Z. He, Y. Alexeev, and I. Safro, Constructing optimal contraction trees for tensor network quantum circuit simulation, in 2022 IEEE High Performance Extreme Computing Conference (HPEC) (2022) pp. 1–8
2022
Later among the works it cites.
A. Bärtschi and S. Eidenbenz, Short-depth circuits for dicke state preparation, in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (2022) pp. 87–96
2022
Later among the works it cites.
Z. H. Saleem, T. Tomesh, B. Tariq, and M. Suchara, Approaches to constrained quantum approximate optimization, SN Computer Science 4
2023
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E. Pelofske, A. Bärtschi, and S. Eidenbenz, Quantum annealing vs. QAOA: 127 qubit higher-order ising problems on NISQ computers, in High Performance Computing (Springer Nature Switzerland, Cham, 2023) pp. 240–258
2023
Closest in time.
2023
Closest in time.
S. Brandhofer, D. Braun, V. Dehn, G. Hellstern, M. Hüls, Y. Ji, I. Polian, A. S. Bhatia, and T. Wellens, Benchmarking the performance of portfolio optimization with QAOA, Quantum Information Processing 22
2023
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R. Tate, J. Moondra, B. Gard, G. Mohler, and S. Gupta, Warm-started QAOA with custom mixers provably converges and computationally beats Goemans-Williamson’s Max-Cut at low circuit depths, Quantum 7
2023
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2023
Closest in time.
2023
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L. P. García-Pintos, L. T. Brady, J. Bringewatt, and Y.-K. Liu, Lower bounds on quantum annealing times, Phys. Rev. Lett. 130
2023
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P. Chandarana, N. N. Hegade, I. Montalban, E. Solano, and X. Chen, Digitized counterdiabatic quantum algorithm for protein folding, Phys. Rev. Appl. 20
2023
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A. Gonzales, R. Shaydulin, Z. H. Saleem, and M. Suchara, Quantum error mitigation by Pauli check sandwiching, Scientific Reports 13
2023
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S. Boulebnane, X. Lucas, A. Meyder, S. Adaszewski, and A. Montanaro, Peptide conformational sampling using the quantum approximate optimization algorithm, npj Quantum Information 9
2023
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S. Aktar, A.-H. A. Badawy, A. Bärtschi, and S. Eidenbenz, Scalable experimental bounds for dicke and ghz states fidelities, in Proceedings of the 20th ACM International Conference on Computing Frontiers (Association for Computing Machinery, New York, NY, USA, 2023) p. 176–184
2023
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