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Variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) computers.
E. Fradkin, “Jordan-Wigner transformation for quantum-spin systems in two dimensions and fractional statistics,” Physical Review Letters , vol. 63, pp. 322–325, July 1989. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.63.322
1989
Earlier work this paper cites.
K. Kowalski and P. Piecuch, “Renormalized CCSD(T) and CCSD(TQ) approaches: Dissociation of the N2 triple bond,” The Journal of Chemical Physics , vol. 113, p. 5644, 2000. [Online]. Available: https://doi.org/10.1063/1.1290609
2000
Earlier work this paper cites.
H. Wendland, Scattered data approximation . Cambridge university press, 2004, vol. 17
2004
Earlier work this paper cites.
A. Klamt, COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design . Elsevier Science, 2005. [Online]. Available: https://books.google.co.jp/books?id=1vb2lAEACAAJ
2005
Earlier work this paper cites.
C. E. Rasmussen, C. K. Williams et al. , Gaussian processes for machine learning . Springer, 2006, vol. 1
2006
Earlier work this paper cites.
E. Knill, D. Leibfried, R. Reichle, J. Britton, R. B. Blakestad, J. D. Jost, C. Langer, R. Ozeri, S. Seidelin, and D. J. Wineland, “Randomized benchmarking of quantum gates,” Phys. Rev. A , vol. 77, p. 012307, Jan 2008. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevA.77.012307
2008
Earlier work this paper cites.
N. Srinivas, A. Krause, S. Kakade, and M. Seeger, “Gaussian process optimization in the bandit setting: no regret and experimental design,” in Proceedings of the 27th International Conference on International Conference on Machine Learning , 2010, pp. 1015–1022
2010
Earlier work this paper cites.
A. Agarwal, O. Dekel, and L. Xiao, “Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback.” in Colt . Citeseer, 2010, pp. 28–40
2010
Earlier work this paper cites.
A. Van Der Vaart and H. Van Zanten, “Information Rates of Nonparametric Gaussian Process Methods.” Journal of Machine Learning Research , vol. 12, no. 6, 2011
2011
Earlier work this paper cites.
G. Knizia and G. K.-L. Chan, “Density matrix embedding: A simple alternative to dynamical mean-field theory,” Phys. Rev. Lett. , vol. 109, p. 186404, November 2012. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.109.186404
2012
Earlier work this paper cites.
A. D. Bochevarov, E. Harder, T. F. Hughes, J. R. Greenwood, D. A. Braden, D. M. Philipp, D. Rinaldo, M. D. Halls, J. Zhang, and R. A. Friesner, “Jaguar: A high-performance quantum chemistry software program with strengths in life and materials sciences,” International Journal of Quantum Chemistry , vol. 113, no. 18, pp. 2110–2142, 2013. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/qua.24481
2013
Earlier work this paper cites.
A. Peruzzo, J. McClean, P. Shadbolt, M. H. Yung, X. Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, “A variational eigenvalue solver on a photonic quantum processor,” Nature Communications 2014 5:1 , vol. 5, pp. 1–7, July 2014. [Online]. Available: https://www.nature.com/articles/ncomms5213
2014
Cited alongside, same era.
S. Bubeck et al. , “Convex optimization: Algorithms and complexity,” Foundations and Trends® in Machine Learning , vol. 8, no. 3-4, pp. 231–357, 2015
2015
Cited alongside, same era.
2017
Cited alongside, same era.
S. R. Chowdhury and A. Gopalan, “On kernelized multi-armed bandits,” in International Conference on Machine Learning . PMLR, 2017, pp. 844–853
2017
Cited alongside, same era.
Y. Kawashima, E. Lloyd, M. P. Coons, Y. Nam, S. Matsuura, A. J. Garza, S. Johri, L. Huntington, V. Senicourt, A. O. Maksymov, J. H. V. Nguyen, J. Kim, N. Alidoust, A. Zaribafiyan, and T. Yamazaki, “Optimizing electronic structure simulations on a trapped-ion quantum computer using problem decomposition,” Communications Physics , vol. 4, p. 245, 2021. [Online]. Available: https://doi.org/10.1038/s42005-021-00751-9
2021
Later among the works it cites.
Q. Developers, “Qiskit: An open-source framework for quantum computing,” 2021
2021
Later among the works it cites.
H. Shang, L. Shen, Y. Fan, Z. Xu, C. Guo, J. Liu, W. Zhou, H. Ma, R. Lin, Y. Yang, F. Li, Z. Wang, Y. Zhang, and Z. Li, “Large-scale simulation of quantum computational chemistry on a new sunway supercomputer,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis . IEEE Computer Society, 11 2022, pp. 1–14. [Online]. Available: https://doi.ieeecomputersociety.org/
2022
Later among the works it cites.
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G. Santin and B. Haasdonk, “Convergence rate of the data-independent P-greedy algorithm in kernel-based approximation,” Dolomites Research Notes on Approximation , vol. 10, no. Special_Issue, 2017
2017
Cited alongside, same era.
Q. Sun, T. C. Berkelbach, N. S. Blunt, G. H. Booth, S. Guo, Z. Li, J. Liu, J. D. McClain, E. R. Sayfutyarova, S. Sharma, S. Wouters, and G. K.-L. Chan, “PySCF: the Python-based simulations of chemistry framework,” WIREs Computational Molecular Science , vol. 8, no. 1, p. e1340, 2018. [Online]. Available: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1340
2018
Cited alongside, same era.
A. Durand, O.-A. Maillard, and J. Pineau, “Streaming kernel regression with provably adaptive mean, variance, and regularization,” The Journal of Machine Learning Research , vol. 19, no. 1, pp. 650–683, 2018
2018
Cited alongside, same era.
M. Mutny and A. Krause, “Efficient high dimensional bayesian optimization with additivity and quadrature fourier features,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Cited alongside, same era.
M. Kühn, S. Zanker, P. Deglmann, M. Marthaler, and H. Weiß, “Accuracy and Resource Estimations for Quantum Chemistry on a Near-Term Quantum Computer,” Journal of Chemical Theory and Computation , vol. 15, pp. 4764–4780, 2019. [Online]. Available: https://pubs.acs.org/sharingguidelines
2019
Cited alongside, same era.
K. M. Nakanishi, K. Fujii, and S. Todo, “Sequential minimal optimization for quantum-classical hybrid algorithms,” Physical Review Research , vol. 2, no. 4, p. 043158, 2020
2020
Cited alongside, same era.
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,” Nature Reviews Physics , vol. 3, pp. 625–644, 2021. [Online]. Available: https://doi.org/10.1038/s42254-021-00348-9
2021
Cited alongside, same era.
J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Rungger, G. H. Booth, and J. Tennyson, “The Variational Quantum Eigensolver: a review of methods and best practices,” Physics Reports , vol. 986, pp. 1–128, November 2022. [Online]. Available: https://doi.org/10.1016/j.physrep.2022.08.003
2022
Later among the works it cites.
J. F. Gonthier, M. D. Radin, C. Buda, E. J. Doskocil, C. M. Abuan, and J. Romero, “Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis,” Physical Review Research , vol. 4, p. 033154, August 2022. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.4.033154
2022
Later among the works it cites.
G. Iannelli and K. Jansen, “Noisy Bayesian optimization for variational quantum eigensolvers,” in The 38th International Symposium on Lattice Field Theory , 2022, p. 251
2022
Later among the works it cites.
J. Müller, W. Lavrijsen, C. Iancu, and W. de Jong, “Accelerating Noisy VQE Optimization with Gaussian Processes,” in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) . IEEE, 2022, pp. 215–225
2022
Later among the works it cites.
2022
Later among the works it cites.
IBM, “Our new 2022 Development Roadmap,” 2022, last checked: April, 2023. [Online]. Available: https://www.ibm.com/quantum/roadmap
2023
Closest in time.
IBM, “Measurement Error Mitigation,” 2021, last checked: September, 2023. [Online]. Available: https://qiskit.org/documentation/stable/0.26/tutorials/noise/3_measurement_error_mitigation.html
2023
Closest in time.