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Variational quantum eigensolver(VQE) typically minimizes energy with hybrid quantum-classical optimization, which aims to find the ground state.
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Jarrod R. McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics 18
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Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini, “Parameterized quantum circuits as machine learning models,” Quantum Science and Technology 4
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2017
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Yangchao Shen, Xiang Zhang, Shuaining Zhang, Jing-Ning Zhang, Man-Hong Yung, and Kihwan Kim, “Quantum implementation of the unitary coupled cluster for simulating molecular electronic structure,” Phys. Rev. A 95
2017
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Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M. Chow, and Jay M. Gambetta, “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,” Nature 549
2017
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Cornelius Hempel, Christine Maier, Jonathan Romero, Jarrod McClean, Thomas Monz, Heng Shen, Petar Jurcevic, Ben P. Lanyon, Peter Love, Ryan Babbush, Alán Aspuru-Guzik, Rainer Blatt, and Christian F. Roos, “Quantum chemistry calculations on a trapped-ion quantum simulator,” Physical Review X 8
2018
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2018
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K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, “Quantum circuit learning,” Phys. Rev. A 98
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Nikolaj Moll, Panagiotis Barkoutsos, Lev S. Bishop, Jerry M. Chow, Andrew Cross, Daniel J. Egger, Stefan Filipp, Andreas Fuhrer, Jay M. Gambetta, Marc Ganzhorn, Abhinav Kandala, Antonio Mezzacapo, Peter Müller, Walter Riess, Gian Salis, John Smolin, Ivano Tavernelli, and Kristan Temme, “Quantum optimization using variational algorithms on near-term quantum devices,” Quantum Science and Technology 3
2018
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2018
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Ken M. Nakanishi, Kosuke Mitarai, and Keisuke Fujii, “Subspace-search variational quantum eigensolver for excited states,” Physical Review Research 1
2019
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Filippo Vicentini, Alberto Biella, Nicolas Regnault, and Cristiano Ciuti, “Variational neural-network ansatz for steady states in open quantum systems,” Phys. Rev. Lett. 122
2019
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Eyal Bairey, Itai Arad, and Netanel H. Lindner, “Learning a local hamiltonian from local measurements,” Phys. Rev. Lett. 122
2019
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Xiao-Liang Qi and Daniel Ranard, “Determining a local Hamiltonian from a single eigenstate,” Quantum 3
2019
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Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran, “Evaluating analytic gradients on quantum hardware,” Phys. Rev. A 99
2019
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2019
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Tyler Takeshita, Nicholas C. Rubin, Zhang Jiang, Eunseok Lee, Ryan Babbush, and Jarrod R. McClean, “Increasing the representation accuracy of quantum simulations of chemistry without extra quantum resources,” Phys. Rev. X 10
2020
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Dan-Bo Zhang and Tao Yin, “Collective optimization for variational quantum eigensolvers,” Phys. Rev. A 101
2020
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Michael Lubasch, Jaewoo Joo, Pierre Moinier, Martin Kiffner, and Dieter Jaksch, “Variational quantum algorithms for nonlinear problems,” Phys. Rev. A 101
2020
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Jordan Cotler and Frank Wilczek, “Quantum overlapping tomography,” Phys. Rev. Lett. 124
2020
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2020
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2020
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