Quantum optimization with a novel Gibbs objective function and ansatz architecture search
Open accessAbstract
This work proposes modifications on two key components of the Quantum Approximate Optimization Algorithm (QAOA): a Gibbs objective function and an ansatz architecture search algorithm. The authors explore how these propositions improve upon ordinary QAOA when finding low-energy states by using numerical experiments on grid and complete graph Ising models.
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All of the objective function approaches (CVar, ascending CVar, Gibbs) serve to foster low-energy solutions and simultaneously avoid getting trapped in local minima.
“All of these approaches serve to foster low-energy solutions and simultaneously avoid to get trapped in local minima.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [86]
The Theory section (page not available) says the Gibbs objective rewards increased probability of low-energy outcomes and mentions CVaR as another objective function. It does not discuss ascending CVaR or establish that these approaches avoid trapping the optimization in local minima. (LLM, conf=0.87)
Experimental — accuracy not yet measuredArchitecture search approaches that adapt the ansatz of a variational approach may take an existing ansatz as a starting point or be more generally created.
“Likewise, architecture search approaches that adapt the ansatz of the variational approach may take an existing ansatz as a starting point [86, 89, 155] or be more generally created [88, 96, 97, 151, 152].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [86]
CVar, ascending CVar, and the Gibbs objective function can be regarded as adaptations of standard QAOA or VQE that affect their underlying objective function.
“In this sense, CVar [10], ascending CVar [78], and the Gibbs objective function [86] can be regarded as adaptations of standard QAOA or VQE that affect their underlying objective function.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [86]
Proposals to overcome fixed and rigid ansatz restrictions comprise methods that search in a generally unrestricted search space and approaches that take existing quantum algorithms as a starting point.
“Proposals to overcome these restrictions comprise methods that search in a generally unrestricted search space [97, 151, 152], but also approaches that take existing quantum algorithms as a starting point [86, 155], as it will be further described in Section 4.1.3.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [86]
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Cites
A Simplex Method for Function Minimization
Nelder, J. A., & Mead, R. (1965) · The Computer Journal · doi:10.1093/comjnl/7.4.308 · Cited by 28,905 · see citing works
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830 · see citing works
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552 · cited 2× · see citing works
“…and opportunities for further developments: The Gibbs objective function is easy to implement for the combinatorial optimization problems we considered here, but may be useful more broadly for quantum optimization problems, such as variational approaches to molecular ground states [17, 20].”
MethodDistilling Free-Form Natural Laws from Experimental Data
Schmidt, M., & Lipson, H. (2009) · Science · doi:10.1126/science.1165893 · Cited by 3,327 · see citing works
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279 · see citing works
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440 · see citing works
The theory of variational hybrid quantum-classical algorithms
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/2/023023 · Cited by 2,227 · see citing works
Advances in Neural Information Processing Systems 19 (NIPS 2006)
Simonotto, E., Whalley, H. C., Lawrie, S. M., Murray, L. M., McGonigle, D. J., & Storkey, A. (2006) · Cited by 1,651 · see citing works
AI Feynman: A physics-inspired method for symbolic regression
Udrescu, S., & Tegmark, M. (2019) · Science Advances · doi:10.1126/sciadv.aay2631 · Cited by 1,470 · see citing works
Optimization of Molecules via Deep Reinforcement Learning
Zhou, Z., Kearnes, S. M., Li, L., Zare, R. N., & Riley, P. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-47148-x · Cited by 602 · see citing works
Self-citationGu, G. X., Chen, C.-T., Richmond, D. J., & Buehler, M. J. (2018) · Materials Horizons · doi:10.1039/c8mh00653a · Cited by 556 · see citing works
Random Search and Reproducibility for Neural Architecture SearchPreprint
Li, L., & Talwalkar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07638 · Cited by 534 · see citing works
Self-citationExploring Randomly Wired Neural Networks for Image Recognition
Xie, S., Kirillov, A., Girshick, R., & He, K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00137 · Cited by 355 · see citing works
Quantum Supremacy through the Quantum Approximate Optimization Algorithm
Farhi, E., & Harrow, A. W. (2016) · arXiv · doi:10.48550/arxiv.1602.07674 · Cited by 331 · see citing works
Improving Variational Quantum Optimization using CVaR
Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., & Woerner, S. (2020) · Quantum · doi:10.22331/q-2020-04-20-256 · Cited by 263 · see citing works
Leveraging Grammar and Reinforcement Learning for Neural Program SynthesisPreprint
Bunel, R., Hausknecht, M., Devlin, J., Singh, R., & Kohli, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.04276 · Cited by 94 · see citing works
Brandao, F. G. S. L., Broughton, M., Farhi, E., Gutmann, S., & Neven, H. (2018) · arXiv · doi:10.48550/arxiv.1812.04170 · Cited by 88 · see citing works
Exploring Randomly Wired Neural Networks for Image RecognitionPreprint
Xie, S., Kirillov, A., Girshick, R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01569 · Cited by 79 · see citing works
A universal training algorithm for quantum deep learning
Verdon, G., Pye, J., & Broughton, M. (2018) · arXiv · doi:10.48550/arxiv.1806.09729 · Cited by 68 · see citing works
Optimizing QAOA: Success Probability and Runtime Dependence on Circuit DepthPreprint
Niu, M. Y., Lu, S., & Chuang, I. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12134 · Cited by 32 · see citing works
McCullagh, P. J., & Kolassa, J. E. (2009) · Scholarpedia · doi:10.4249/scholarpedia.4699 · Cited by 15 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- E. Farhi, J. Goldstone, and S. Gutmann, arXiv:1411.4028. · arXiv
- E. Farhi, J. Goldstone, and S. Gutmann, arXiv:1412.6062. · arXiv
- B. Zoph and Q. V. Le, in Proceedings of 2017 Interna- tional Conference on Learning Representations (OpenReview, 2017).
- L. Li and A. Talwalkar, in Proceedings of the 2019 Conference on Uncertainty in Artificial Intelligence (2019), http://auai.org/ uai2019/accepted.php.
- S.-M. Udrescu and M. Tegmark, arXiv:1905.11481. · arXiv
- K. Deb, in Search Methodologies (Springer, Boston, MA, 2014), pp. 403-449.
- Z. Li, Q. Chen, and V. Koltun, in Advances in Neural Infor- mation Processing Systems 31 (NIPS 2018) (Curran Associates, Red Hook, NY, 2018), pp. 539-548.
- R. Bunel, M. Hausknecht, J. Devlin, R. Singh, and P. Kohli, in Proceedings of 2018 International Conference on Learning Representations (OpenReview, 2018).
Cited by
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Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 1,726 · see citing works
A review on Quantum Approximate Optimization Algorithm and its variants
Blekos, K., Brand, D., Ceschini, A., Chou, C.-H., Li, R.-H., Pandya, K., & Summer, A. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.03.002 · Cited by 444 · see citing works
Zhu, L., Tang, H. L., Barron, G. S., Calderon-Vargas, F. A., Mayhall, N. J., Barnes, E., & Economou, S. E. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.033029 · Cited by 245 · see citing works
Challenges and opportunities in quantum optimization
Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., et al. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00770-9 · Cited by 201 · see citing works
“Another cost function is the Gibbs objective function 76 .”
Quantum circuit architecture search for variational quantum algorithms
Du, Y., Huang, T., You, S., Hsieh, M.-H., & Tao, D. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00570-y · Cited by 199 · see citing works
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Zhang, S., Hsieh, C., Zhang, S., & Yao, H. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac87cd · Cited by 170 · see citing works
Shaydulin, R., Li, C., Chakrabarti, S., DeCross, M., Herman, D., Kumar, N., Larson, J., Lykov, D., Minssen, P., Sun, Y., et al. (2024) · Science Advances · doi:10.1126/sciadv.adm6761 · Cited by 120 · see citing works
“(78 and 79), though the difference observed between the two figures of merit is more drastic in our case due to the hardness of the problem considered.”
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Tian, J., Sun, X., Du, Y., Zhao, S., Liu, Q., Zhang, K., Yi, W., Huang, W., Wang, C., Wu, X., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3272029 · Cited by 116 · see citing works
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Li, W., & Deng, D.-L. (2021) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-021-1793-6 · Cited by 106 · see citing works
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Wang, Y., & Liu, J. (2024) · Reports on Progress in Physics · doi:10.1088/1361-6633/ad7f69 · Cited by 111 · see citing works
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Yao, J., Lin, L., & Bukov, M. (2021) · Physical Review X · doi:10.1103/physrevx.11.031070 · Cited by 78 · see citing works
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Boulebnane, S., & Montanaro, A. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.030348 · Cited by 57 · see citing works
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Zhang, S., Wan, Z., Lee, C., Hsieh, C., Zhang, S., & Yao, H. (2022) · Physical Review Letters · doi:10.1103/physrevlett.128.120502 · Cited by 54 · see citing works
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Lu, Z., Shen, P.-X., & Deng, D.-L. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.044039 · Cited by 48 · see citing works
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“These questions also tie intimately to other aspects, such as the definition of the cost function [22, 23], and the hybrid quantum-classical algorithms which we use to optimize the variational state [24–31].”
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“But also greedy heuristics have been proposed for the purpose of ansatz search [86].”
BackgroundMethodInfluentialSikiru, I. A., Kora, A. D., Ezin, E. C., Imoize, A. L., & Li, C. (2024) · Electronics · doi:10.3390/electronics13214153 · Cited by 23 · see citing works
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Grange, C., Poss, M., & Bourreau, E. (2023) · 4OR · doi:10.1007/s10288-023-00549-1 · Cited by 21 · see citing works
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Li, Y., Zhang, J., Zhou, R., Qu, Y., & Xu, R. (2025) · Entropy · doi:10.3390/e27070733 · Cited by 5 · see citing works
Enhancing quantum approximate optimization with CNN-CVaR integration
Cai, P., Shen, K., Yang, T., Hu, Y., Lv, B., Fan, L., Liu, Z., Hu, Q., Chen, S., Zhu, Y., et al. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04655-3 · Cited by 5 · see citing works
Bayesian Parameterized Quantum Circuit Optimization (BPQCO): a task and hardware-dependent approach
Benítez-Buenache, A., & Portell-Montserrat, Q. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00308-x · Cited by 5 · see citing works
Quantum mean-value approximator for hard integer-value problems
Joseph, D., Martinez, A., Ling, C., & Mintert, F. (2022) · Physical Review A · doi:10.1103/physreva.105.052419 · Cited by 4 · see citing works
Gsqas: Graph Self-Supervised Quantum Architecture SearchPreprint
He, Z., Deng, M., Zheng, S., Li, L., & Situ, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4501980 · Cited by 4 · see citing works
Self-citationQuantum Optimization in Engineering: A Brief Review
Mahmoudi, Y., Zioui, N., & Belbachir, H. (2023) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa59624.2023.10286779 · Cited by 3 · see citing works
Simulating adiabatic quantum computing with parameterized quantum circuits
Kolotouros, I., Petrongonas, I., Prokop, M., & Wallden, P. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad80c0 · Cited by 3 · see citing works
A coherent approach to quantum-classical optimization
Cáliz, A. N., Riu, J., Bosch, J., Torrente, P., Miralles, J., & Riera, A. (2025) · Communications Physics · doi:10.1038/s42005-025-02111-3 · Cited by 4 · see citing works
Okawa, H. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-0065-3_21 · Cited by 3 · see citing works
Applying analog quantum computing to solve optimisation problems: case studies on Max-Cut and CVRP
Deleplanque, S., Yaagoubi, A. E., Gras, T., Descamps, F., Mourrier, J., & Lefebvre, I. (2025) · International Journal of Systems Science Operations & Logistics · doi:10.1080/23302674.2025.2463530 · Cited by 3 · see citing works
Tabu-Driven Quantum Neighborhood Samplers
Moussa, C., Wang, H., Calandra, H., Bäck, T., & Dunjko, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72904-2_7 · Cited by 3 · see citing works
Tabu-driven Quantum Neighborhood SamplersPreprint
Moussa, C., Wang, H., Calandra, H., Bäck, T. H. W., & Dunjko, V. (2020) · Leiden Repository (Leiden University) · Cited by 3 · see citing works
Soloviev, V. P., Dunjko, V., Bielza, C., Larrañaga, P., & Wang, H. (2024) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-024-00282-6 · Cited by 2 · see citing works
Multi-target quantum compilation algorithm
Hai, V. T., Viet, N. T., Urbaneja, J., Linh, N. V., Tran, L. N., & Ho, L. B. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad9705 · Cited by 3 · see citing works
Hybrid GRU-CNN bilinear parameters initialization for quantum approximate optimization algorithm
Xu, Z., Cai, P., Shen, K., Yang, T., Hu, Y., Gong, M., Zhu, Y., Wu, Z., Dai, Y., & Yang, F. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad5a50 · Cited by 2 · see citing works
Ohno, H. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00221-9 · Cited by 3 · see citing works
Alonso, M., Martínez, R., Díez-Valle, P., Garbayo, A., García‐Santiago, X., & Gil, G. B. (2025) · IEEE Access · doi:10.1109/access.2025.3578780 · Cited by 2 · see citing works
An introduction to variational quantum algorithms for combinatorial optimization problemsPreprint
Grange, C., Poss, M., & Bourreau, E. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.11734 · Cited by 2 · see citing works
Finding dense sublattices as low energy states of a Hamiltonian
Barberà-Rodríguez, J., Gama, N., Narayanan, A. K., & Joseph, D. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043279 · Cited by 1 · see citing works
Optimization driven quantum circuit reduction
Rosenhahn, B., Osborne, T. J., & Hirche, C. (2025) · New Journal of Physics · doi:10.1088/1367-2630/ae0e40 · Cited by 1 · see citing works
Generating Generalized Ground-State Ansätze from Few-Body Examples
Lourens, M., Sinayskiy, I., Kriel, J. N., & Petruccione, F. (2025) · Physical Review Letters · doi:10.1103/5vc5-9f4d · Cited by 1 · see citing works
Metamodel and Formalization to Model, Transform, Deploy, and Execute Quantum Workflows
Beisel, M., Barzen, J., Bechtold, M., Leymann, F., Truger, F., & Weder, B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-68165-3_6 · Cited by 1 · see citing works
Improving the Quantum Approximate Optimization Algorithm with postselectionPreprint
Boulebnane, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05425 · Cited by 1 · see citing works
Multi-target quantum compilation algorithmPreprint
Ho, L. B., Hai, V. T., Viet, N. T., Urbaneja, J., Linh, N. V., & Tran, L. N. (2024) · Research Square · doi:10.21203/rs.3.rs-4678149/v1 · Cited by 1 · see citing works
An Analysis of the Quantum Approximation Optimisation AlgorithmPreprint
Mansouri, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12791
Graph Optimization Studies with QAOA Using Pennylane
Sesay, A. M., Devanur, S. G., Perumal, T., & Prakash, K. B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-61929-8_31
Quantum information processing for quantum simulations
Calonnec, C. L. (2023) · theses.fr (ABES) · doi:10.70675/b46267d0z0466z489dzbda2z220bc68889cb
- Authors
- Li, L., Fan, M., Coram, M., Riley, P., & Leichenauer, S.
- Affiliations
- Google (United States) (US)
- Venue
- Physical Review Research
- Publisher
- American Physical Society (APS)
- Published
- 2020-04-24
- Volume
- 2
- Issue
- 2
- ISSN
- 2643-1564 (ISSN-L)
- OpenAlex
- W2974416503
- Cited by
- 98
- References
- 23
- Citations by year
- 2025: 9 · 2024: 32 · 2023: 17 · 2022: 19 · 2021: 13 · 2020: 7 · 2019: 1
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Physical Review Research (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Association for Asian Studies
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- quantum approximate optimization algorithm, quantum optimization, Ising model, low-energy states, variational quantum algorithms
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