Fetching the paper…
Reading the bibliography…
Quantum optimization solvers typically rely on one-variable-to-one-qubit mapping.
D. Sherrington and S. Kirkpatrick, Solvable model of a spin-glass, Phys. Rev. Lett. 35
1975
Earlier work this paper cites.
G. Parisi, Infinite number of order parameters for spin-glasses, Phys. Rev. Lett. 43
1979
Earlier work this paper cites.
D. C. Liu and J. Nocedal, On the limited memory bfgs method for large scale optimization, Math. Program. 45
1989
Earlier work this paper cites.
D. J. Wales and J. P. K. Doye, Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms, J. Phys. Chem. A 101
1997
Earlier work this paper cites.
E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, Quantum computation by adiabatic evolution, arXiv , 0001106 (2000)
2000
Earlier work this paper cites.
S. Boettcher, Extremal optimization for sherrington-kirkpatrick spin glasses, Eur. Phys. J. B 46
2005
Earlier work this paper cites.
V. Choi, Minor-embedding in adiabatic quantum computation: I. the parameter setting problem, Quantum Inf. Process. 7
2008
Earlier work this paper cites.
F. Glover, Z. Lü, and J.-K. Hao, Diversification-driven tabu search for unconstrained binary quadratic problems, 4OR 8
2010
Earlier work this paper cites.
A. Lucas, Ising formulations of many np problems, Front. Phys. 2
2014
Earlier work this paper cites.
D. A. Lidar, Review of decoherence-free subspaces, noiseless subsystems, and dynamical decoupling, Quantum information and computation for chemistry , 295 (2014)
2014
Earlier work this paper cites.
S. Bravyi, G. Smith, and J. A. Smolin, Trading classical and quantum computational resources, Phys. Rev. X 6
2016
Earlier work this paper cites.
D. Suter and G. A. Álvarez, Colloquium: Protecting quantum information against environmental noise, Rev. Mod. Phys. 88
2016
Earlier work this paper cites.
I. Dunning, S. Gupta, and J. Silberholz, What works best when? a systematic evaluation of heuristics for max-cut and QUBO, INFORMS Journal on Computing 30
2018
Earlier work this paper cites.
J. Roffe, Quantum error correction: an introductory guide, Contemp. Phys. 60
2019
Earlier work this paper cites.
S. Bravyi, A. Kliesch, R. Koenig, and E. Tang, Obstacles to variational quantum optimization from symmetry protection, Phys. Rev. Lett. 125
2020
Earlier work this paper cites.
T. Peng, A. W. Harrow, M. Ozols, and X. Wu, Simulating large quantum circuits on a small quantum computer, Phys. Rev. Lett. 125
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.
M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles, Variational quantum algorithms, Nat. Rev. Phys. 3
2021
Earlier work this paper cites.
D. J. Egger, J. Mareček, and S. Woerner, Warm-starting quantum optimization, Quantum 5
2021
Earlier work this paper cites.
M. P. Harrigan, K. J. Sung, M. Neeley, K. J. Satzinger, F. Arute, K. Arya, J. Atalaya, J. C. Bardin, R. Barends, S. Boixo, et al. , Quantum approximate optimization of non-planar graph problems on a planar superconducting processor, Nat. Phys. 17
2021
Earlier work this paper cites.
W. Tang, T. Tomesh, M. Suchara, J. Larson, and M. Martonosi, Cutqc: using small quantum computers for large quantum circuit evaluations, in Proceedings of the 26th ACM International conference on architectural support for programming languages and operating systems (New York) (ACM, 2021) pp. 473–486
2021
Earlier work this paper cites.
L. Bittel and M. Kliesch, Training variational quantum algorithms is np-hard, Phys. Rev. Lett. 127
2021
Earlier work this paper cites.
J. Wurtz and P. Love, Maxcut quantum approximate optimization algorithm performance guarantees for p > 1 p>1 , Phys. Rev. A 103
2021
Earlier work this paper cites.
B. Y. L. Tan, M.-A. Lemonde, S. Thanasilp, J. Tangpanitanon, and D. G. Angelakis, Qubit-efficient encoding schemes for binary optimisation problems, Quantum 5
2021
Earlier work this paper cites.
L. T. Brady, C. L. Baldwin, A. Bapat, Y. Kharkov, and A. V. Gorshkov, Optimal protocols in quantum annealing and quantum approximate optimization algorithm problems, Phys. Rev. Lett. 126
2021
Cited alongside, same era.
L. Zhu, H. L. Tang, G. S. Barron, F. A. Calderon-Vargas, N. J. Mayhall, E. Barnes, and S. E. Economou, Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer, Phys. Rev. Res. 4
2022
Cited alongside, same era.
S. Ebadi, A. Keesling, M. Cain, T. T. Wang, H. Levine, D. Bluvstein, G. Semeghini, A. Omran, J.-G. Liu, R. Samajdar, et al. , Quantum optimization of maximum independent set using rydberg atom arrays, Science 376
2022
Cited alongside, same era.
S. Krinner, N. Lacroix, A. Remm, A. Di Paolo, E. Genois, C. Leroux, C. Hellings, S. Lazar, F. Swiadek, J. Herrmann, et al. , Realizing repeated quantum error correction in a distance-three surface code, Nature 605
2022
Cited alongside, same era.
Z. Cai, R. Babbush, S. C. Benjamin, S. Endo, W. J. Huggins, Y. Li, J. R. McClean, and T. E. O’Brien, Quantum error mitigation, Rev. Mod. Phys. 95
2023
Later among the works it cites.
K. Blekos, D. Brand, A. Ceschini, C.-H. Chou, R.-H. Li, K. Pandya, and A. Summer, A review on quantum approximate optimization algorithm and its variants, Phys. Rep. 1068
2024
Closest in time.
L. T. Brady and S. Hadfield, Iterative quantum algorithms for maximum independent set, Phys. Rev. A 110
2024
Closest in time.
J. R. Finžgar, A. Kerschbaumer, M. J. A. Schuetz, C. B. Mendl, and H. G. Katzgraber, Quantum-informed recursive optimization algorithms, PRX Quantum 5
2024
Closest in time.
M. Dupont and B. Sundar, Extending relax-and-round combinatorial optimization solvers with quantum correlations, Phys. Rev. A 109
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. L. Patti, J. Kossaifi, A. Anandkumar, and S. F. Yelin, Variational quantum optimization with multibasis encodings, Phys. Rev. Res. 4
2022
Cited alongside, same era.
E. Farhi, J. Goldstone, S. Gutmann, and L. Zhou, The quantum approximate optimization algorithm and the sherrington-kirkpatrick model at infinite size, Quantum 6
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 17th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2022) (Schloss Dagstuhl – Leibniz-Zentrum fur Informatik, 2022) pp. 7:1–7:21
2022
Cited alongside, same era.
U. Azad, B. K. Behera, E. A. Ahmed, P. K. Panigrahi, and A. Farouk, Solving vehicle routing problem using quantum approximate optimization algorithm, IEEE Trans. Intell. Transp. Syst. , 7564 (2022)
2022
Cited alongside, same era.
Y. Chen, L. Zhu, N. J. Mayhall, E. Barnes, and S. E. Economou, How much entanglement do quantum optimization algorithms require?, in Quantum 2.0 (Optica Publishing Group, 2022) pp. QM4A–2
2022
Cited alongside, same era.
M. Dupont, N. Didier, M. J. Hodson, J. E. Moore, and M. J. Reagor, Entanglement perspective on the quantum approximate optimization algorithm, Phys. Rev. A 106
2022
Cited alongside, same era.
A. D. King, J. Raymond, T. Lanting, R. Harris, A. Zucca, F. Altomare, A. J. Berkley, K. Boothby, S. Ejtemaee, C. Enderud, et al. , Quantum critical dynamics in a 5,000-qubit programmable spin glass, Nature 617
2023
Cited alongside, same era.
M. Dupont, B. Evert, M. J. Hodson, B. Sundar, S. Jeffrey, Y. Yamaguchi, D. Feng, F. B. Maciejewski, S. Hadfield, M. S. Alam, et al. , Quantum-enhanced greedy combinatorial optimization solver, Sci. Adv. 9
2023
Cited alongside, same era.
F. B. Maciejewski, S. Hadfield, B. Hall, M. Hodson, M. Dupont, B. Evert, J. Sud, M. S. Alam, Z. Wang, S. Jeffrey, et al. , Design and execution of quantum circuits using tens of superconducting qubits and thousands of gates for dense ising optimization problems, Phys. Rev. Applied 22
2024
Closest in time.
R. Shaydulin, C. Li, S. Chakrabarti, M. DeCross, D. Herman, N. Kumar, J. Larson, D. Lykov, P. Minssen, Y. Sun, et al. , Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem, Sci. Adv. 10
2024
Closest in time.
E. Pelofske, A. Bärtschi, and S. Eidenbenz, Short-depth qaoa circuits and quantum annealing on higher-order ising models, npj Quantum Inf. 10
2024
Closest in time.
D. Bluvstein, S. J. Evered, A. A. Geim, S. H. Li, H. Zhou, T. Manovitz, S. Ebadi, M. Cain, M. Kalinowski, D. Hangleiter, et al. , Logical quantum processor based on reconfigurable atom arrays, Nature 626
2024
Closest in time.
B. Bach, J. Falla, and I. Safro, Mlqaoa: Graph learning accelerated hybrid quantum-classical multilevel qaoa, in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) , Vol. 1 (IEEE, 2024) pp. 1–12
2024
Closest in time.
Y. Chatterjee, E. Bourreau, and M. J. Rančić, Solving various np-hard problems using exponentially fewer qubits on a quantum computer, Phys. Rev. A 109
2024
Closest in time.
E. X. Huber, B. Y. L. Tan, P. R. Griffin, and D. G. Angelakis, Exponential qubit reduction in optimization for financial transaction settlement, EPJ Quantum Technology 11
2024
Closest in time.
I. D. Leonidas, A. Dukakis, B. Tan, and D. G. Angelakis, Qubit efficient quantum algorithms for the vehicle routing problem on noisy intermediate-scale quantum processors, Adv. Quantum Technol. 7
2024
Closest in time.
N. Yanakiev, N. Mertig, C. K. Long, and D. R. M. Arvidsson-Shukur, Dynamic adaptive quantum approximate optimization algorithm for shallow, noise-resilient circuits, Phys. Rev. A 109
2024
Closest in time.
A. C. Nakhl, T. Quella, and M. Usman, Calibrating the role of entanglement in variational quantum circuits, Phys. Rev. A 109
2024
Closest in time.
J. A. Montañez-Barrera and K. Michielsen, Toward a linear-ramp qaoa protocol: evidence of a scaling advantage in solving some combinatorial optimization problems, npj Quantum Inf. 11
2025
Closest in time.
Quantum error correction below the surface code threshold, Nature 638
2025
Closest in time.
K. Fang, M. Zhang, R. Shi, and Y. Li, Dynamic quantum circuit compilation, IEEE Trans.Computers 75
2025
Closest in time.
M. Dupont, B. Sundar, B. Evert, D. E. B. Neira, Z. Peng, S. Jeffrey, and M. J. Hodson, Benchmarking quantum optimization for the maximum-cut problem on a superconducting quantum computer, Phys. Rev. Appl. 23
2025
Closest in time.
M. Sciorilli, L. Borges, T. L. Patti, D. García-Martín, G. Camilo, A. Anandkumar, and L. Aolita, Towards large-scale quantum optimization solvers with few qubits, Nature Communications 16
2025
Closest in time.
M. Larocca, S. Thanasilp, S. Wang, K. Sharma, J. Biamonte, P. J. Coles, L. Cincio, J. R. McClean, Z. Holmes, and M. Cerezo, Barren plateaus in variational quantum computing, Nat. Rev. Phys. , 1 (2025)
2025
Closest in time.
Y. Tene-Cohen, T. Kelman, O. Lev, and A. Makmal, A variational qubit-efficient maxcut heuristic algorithm, npj Quantum Inf. 10.1038/s41534-026-01186-2 (2026)
2026
Closest in time.
M. Podobrii, V. Kuzmin, V. Voloshinov, M. Veshchezerova, and M. R. Perelshtein, Qubit-efficient quantum local search for combinatorial optimization, Adv. Quantum Technol. 9
2026
Closest in time.