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Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied.
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D. Kuhn, P. M. Esfahani, V. A. Nguyen, and S. Shafieezadeh-Abadeh, Wasserstein distributionally robust optimization: Theory and applications in machine learning, in Operations research & management science in the age of analytics (Informs, 2019) pp. 130–166
2019
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2020
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K. J. Sung, J. Yao, M. P. Harrigan, N. C. Rubin, Z. Jiang, L. Lin, R. Babbush, and J. R. McClean, Using models to improve optimizers for variational quantum algorithms, Quantum Science and Technology 5
2020
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R. Harper, S. T. Flammia, and J. J. Wallman, Efficient learning of quantum noise, Nature Physics 16
2020
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J. Liu and H. Zhou, Reliability modeling of NISQ-era quantum computers, in 2020 IEEE international symposium on workload characterization (IISWC) (IEEE, 2020) pp. 94–105
2020
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J. Kirschner, I. Bogunovic, S. Jegelka, and A. Krause, Distributionally robust Bayesian optimization, in International Conference on Artificial Intelligence and Statistics (PMLR, 2020) pp. 2174–2184
2020
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K. Sharma, S. Khatri, M. Cerezo, and P. J. Coles, Noise resilience of variational quantum compiling, New Journal of Physics 22
2020
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W. Lavrijsen, A. Tudor, J. Müller, C. Iancu, and W. De Jong, Classical optimizers for noisy intermediate-scale quantum devices, in 2020 IEEE international conference on quantum computing and engineering (QCE) (IEEE, 2020) pp. 267–277
2020
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M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al. , Variational quantum algorithms, Nature Reviews Physics 3
2021
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D. J. Egger, J. Mareček, and S. Woerner, Warm-starting quantum optimization, Quantum 5
2021
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L. Bittel and M. Kliesch, Training variational quantum algorithms is NP-hard, Physical review letters 127
X. Liu, A. Angone, R. Shaydulin, I. Safro, Y. Alexeev, and L. Cincio, Layer VQE: A variational approach for combinatorial optimization on noisy quantum computers, IEEE Transactions on Quantum Engineering 3
2022
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E. Fontana, M. Cerezo, A. Arrasmith, I. Rungger, and P. J. Coles, Non-trivial symmetries in quantum landscapes and their resilience to quantum noise, Quantum 6
2022
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2022
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Z. He, R. Shaydulin, S. Chakrabarti, D. Herman, C. Li, Y. Sun, and M. Pistoia, Alignment between initial state and mixer improves qaoa performance for constrained optimization, npj Quantum Information 9
2023
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2021
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D. Stilck França and R. Garcia-Patron, Limitations of optimization algorithms on noisy quantum devices, Nature Physics 17
2021
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A. B. Magann, C. Arenz, M. D. Grace, T.-S. Ho, R. L. Kosut, J. R. McClean, H. A. Rabitz, and M. Sarovar, From pulses to circuits and back again: A quantum optimal control perspective on variational quantum algorithms, PRX Quantum 2
2021
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2021
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C. N. Self, K. E. Khosla, A. W. Smith, F. Sauvage, P. D. Haynes, J. Knolle, F. Mintert, and M. Kim, Variational quantum algorithm with information sharing, npj Quantum Information 7
2021
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2022
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M. Cerezo, G. Verdon, H.-Y. Huang, L. Cincio, and P. J. Coles, Challenges and opportunities in quantum machine learning, Nature Computational Science 2
2022
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B. Peng, S. Gulania, Y. Alexeev, and N. Govind, Quantum time dynamics employing the Yang-Baxter equation for circuit compression, Physical Review A 106
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2023
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A. Miessen, P. J. Ollitrault, F. Tacchino, and I. Tavernelli, Quantum algorithms for quantum dynamics, Nature Computational Science 3
2023
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X. Bonet-Monroig, H. Wang, D. Vermetten, B. Senjean, C. Moussa, T. Bäck, V. Dunjko, and T. E. O’Brien, Performance comparison of optimization methods on variational quantum algorithms, Physical Review A 107
2023
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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 Transactions on Quantum Computing 4
2023
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G. De Palma, M. Marvian, C. Rouzé, and D. S. França, Limitations of variational quantum algorithms: a quantum optimal transport approach, PRX Quantum 4
2023
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Z. Hu, R. Wolle, M. Tian, Q. Guan, T. Humble, and W. Jiang, Toward consistent high-fidelity quantum learning on unstable devices via efficient in-situ calibration, in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) , Vol. 1 (IEEE, 2023) pp. 848–858
2023
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J. Zhang, H. Wang, G. S. Ravi, F. T. Chong, S. Han, F. Mueller, and Y. Chen, Disq: Dynamic iteration skipping for variational quantum algorithms, in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) , Vol. 1 (IEEE, 2023) pp. 1062–1073
2023
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2023
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S. Dasgupta, T. S. Humble, and A. Danageozian, Adaptive mitigation of time-varying quantum noise, in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) , Vol. 1 (IEEE, 2023) pp. 99–110
2023
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G. S. Ravi, K. Smith, J. M. Baker, T. Kannan, N. Earnest, A. Javadi-Abhari, H. Hoffmann, and F. T. Chong, Navigating the dynamic noise landscape of variational quantum algorithms with QISMET, in Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (2023) pp. 515–529
2023
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S. Duffield, M. Benedetti, and M. Rosenkranz, Bayesian learning of parameterised quantum circuits, Machine Learning: Science and Technology 4
2023
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S. Tibaldi, D. Vodola, E. Tignone, and E. Ercolessi, Bayesian optimization for QAOA, IEEE Transactions on Quantum Engineering (2023)
2023
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J. E. Kim and Y. Wang, Quantum approximate Bayesian optimization algorithms with two mixers and uncertainty quantification, IEEE Transactions on Quantum Engineering (2023)
2023
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F. Farokhi, Distributionally-robust optimization with noisy data for discrete uncertainties using total variation distance, IEEE Control Systems Letters (2023)
2023
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Y. Pan, Z. He, N. Guo, and Z. Zhang, Distributionally robust circuit design optimization under variation shifts, in 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) (IEEE, 2023) pp. 1–8
2023
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T. Hao, K. Liu, and S. Tannu, Enabling high performance debugging for variational quantum algorithms using compressed sensing, in Proceedings of the 50th Annual International Symposium on Computer Architecture (2023) pp. 1–13
2023
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J. R. Finžgar, M. J. Schuetz, J. K. Brubaker, H. Nishimori, and H. G. Katzgraber, Designing quantum annealing schedules using Bayesian optimization, Physical Review Research 6
2024
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2024
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L. Cheng, Y.-Q. Chen, S.-X. Zhang, and S. Zhang, Quantum approximate optimization via learning-based adaptive optimization, Communications Physics 7
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A. Pérez-Salinas, H. Wang, and X. Bonet-Monroig, Analyzing variational quantum landscapes with information content, npj Quantum Information 10
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