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Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers.
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N. Yamamoto, On the natural gradient for variational quantum eigensolver, arXiv:1909.05074 (2019)
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2019
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J. Liu, H. Yuan, X.-M. Lu, and X. Wang, Quantum fisher information matrix and multiparameter estimation, Journal of Physics A: Mathematical and Theoretical 53
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S. Sim, P. D. Johnson, and A. Aspuru-Guzik, Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms, Advanced Quantum Technologies 2
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2019
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H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall, An adaptive variational algorithm for exact molecular simulations on a quantum computer, Nature communications 10
2019
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M. Schuld and N. Killoran, Quantum machine learning in feature hilbert spaces, Physical review letters 122
2019
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V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, Supervised learning with quantum-enhanced feature spaces, Nature 567
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2020
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C. Blank, D. K. Park, J.-K. K. Rhee, and F. Petruccione, Quantum classifier with tailored quantum kernel, npj Quantum Information 6
2020
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A. Mari, T. R. Bromley, and N. Killoran, Estimating the gradient and higher-order derivatives on quantum hardware, Physical Review A 103
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Z.-J. Zhang, T. H. Kyaw, J. Kottmann, M. Degroote, and A. Aspuru-Guzik, Mutual information-assisted adaptive variational quantum eigensolver, Quantum Science and Technology (2021)
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M. Schuld, R. Sweke, and J. J. Meyer, Effect of data encoding on the expressive power of variational quantum-machine-learning models, Physical Review A 103
2021
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