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The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm for finding the minimum eigenvalue of a Hamiltonian that involves the optimization of a parameterized quantum circuit.
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Self-consistent molecular orbital methods. XVII. Geometries and binding energies of second-row molecules. a comparison of three basis sets
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Gradient convergence in gradient methods with errors
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Fermionic quantum computation
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Bounds for the adiabatic approximation with applications to quantum computation
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The Bravyi-Kitaev transformation for quantum computation of electronic structure
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, and Jeremy L. O’Brien · 2014
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Coordinate descent algorithms
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The theory of variational hybrid quantum-classical algorithms
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Tapering off qubits to simulate fermionic hamiltonians
Sergey Bravyi, Jay M. Gambetta, Antonio Mezzacapo, and Kristan Temme · 2017
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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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Generalization of the output of a variational quantum eigensolver by parameter interpolation with a low-depth ansatz
Kosuke Mitarai, Tennin Yan, and Keisuke Fujii · 2019
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Performance of hybrid quantum-classical variational heuristics for combinatorial optimization
Giacomo Nannicini · 2019
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
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Theory of variational quantum simulation
Xiao Yuan, Suguru Endo, Qi Zhao, Ying Li, and Simon C. Benjamin · 2019
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Demonstration of adiabatic variational quantum computing with a superconducting quantum coprocessor
Ming-Cheng Chen, Ming Gong, Xiaosi Xu, Xiao Yuan, Jian-Wen Wang, Can Wang, Chong Ying, Jin Lin, Yu Xu, Yulin Wu, et al · 2020
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Tameem Albash and Daniel A. Lidar · 2018
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Quantum algorithms for electronic structure calculations: Particle-hole hamiltonian and optimized wave-function expansions
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Addressing hard classical problems with adiabatically assisted variational quantum eigensolvers
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Quantum optimization using variational algorithms on near-term quantum devices
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Q. M. Sun, T. C. Berkelbach, N. S. Blunt, G. H. Booth, S. Guo, Z. D. Li, J. Z. Liu, J. D. McClain, E. R. Sayfutyarova, S. Sharma, S. Wouters, and G. K. L. Chan · 2018
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Qiskit: An open-source framework for quantum computing, 2019
Héctor Abraham et al · 2019
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Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition
Gavin E. Crooks · 2019
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Daniel J. Egger, Jakub Marecek, and Stefan Woerner · 2020
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Classical optimizers for noisy intermediate-scale quantum devices
Wim Lavrijsen, Ana Tudor, Juliane Müller, Costin Iancu, and Wibe de Jong · 2020
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Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods
Nicolas Loizou and Peter Richtárik · 2020
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Theory of analytical energy derivatives for the variational quantum eigensolver
Kosuke Mitarai, Yuya O. Nakagawa, and Wataru Mizukami · 2020
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Sequential minimal optimization for quantum-classical hybrid algorithms
Ken M. Nakanishi, Keisuke Fujii, and Synge Todo · 2020
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Kevin J. Sung, Matthew P. Harrigan, Nicholas C. Rubin, Zhang Jiang, Ryan Babbush, and Jarrod R. McClean · 2020
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Meta-variational quantum eigensolver: Learning energy profiles of parameterized hamiltonians for quantum simulation
Alba Cervera-Lierta, Jakob S. Kottmann, and Alán Aspuru-Guzik · 2021
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Variationally scheduled quantum simulation
Shunji Matsuura, Samantha Buck, Valentin Senicourt, and Arman Zaribafiyan · 2021
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