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Hybrid variational quantum algorithms, which combine a classical optimizer with evaluations on a quantum chip, are the most promising candidates to show quantum advantage on current noisy, intermediate-scale quantum (NISQ) devices.
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Ken M. Nakanishi, Keisuke Fujii, and Synge Todo · 2020
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Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
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Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M. Chow, and Jay M. Gambetta · 2017
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Qiskit: An open-source framework for quantum computing, 2021
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Variational quantum algorithms
M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles · 2021
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Noisy bayesian optimization for variational quantum eigensolvers, 2021
Giovanni Iannelli and Karl Jansen · 2021
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Tutotial: Workflow for hybrid quantum-classical algorithm, https://github.com/scikit-quant/scikit-quant/tree/master/tutorials
W. Lavrijsen, J. Müller, and E. Younis · 2021
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Stochastic gradient line bayesian optimization: Reducing measurement shots in optimizing parameterized quantum circuits, 2021
Shiro Tamiya and Hayata Yamasaki · 2021
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The variational quantum eigensolver: a review of methods and best practices, 2021
Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H. Booth, and Jonathan Tennyson · 2021
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Surrogate-based optimization for variational quantum algorithms, 2022
Ryan Shaffer, Lucas Kocia, and Mohan Sarovar · 2022
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