Quantum supremacy using a programmable superconducting processor F Arute, K Arya, R Babbush, D Bacon, JC Bardin, R Barends, R Biswas, ... Nature 574 (7779), 505-510, 2019 | 7988 | 2019 |
A variational eigenvalue solver on a photonic quantum processor A Peruzzo, J McClean, P Shadbolt, MH Yung, XQ Zhou, PJ Love, ... Nature communications 5 (4213), 2014 | 4206 | 2014 |
Variational quantum algorithms M Cerezo, A Arrasmith, R Babbush, SC Benjamin, S Endo, K Fujii, ... Nature Reviews Physics 3 (9), 625-644, 2021 | 2275 | 2021 |
The theory of variational hybrid quantum-classical algorithms JR McClean, J Romero, R Babbush, A Aspuru-Guzik New Journal of Physics 18, 023023, 2016 | 2151 | 2016 |
Barren plateaus in quantum neural network training landscapes JR McClean, S Boixo, VN Smelyanskiy, R Babbush, H Neven Nature Communications 9, 4812, 2018 | 1831 | 2018 |
Scalable Quantum Simulation of Molecular Energies PJJ O'Malley, R Babbush, ID Kivlichan, J Romero, JR McClean, ... Physical Review X 6 (3), 031007, 2016 | 1234 | 2016 |
Quantum chemistry calculations on a trapped-ion quantum simulator C Hempel, C Maier, J Romero, J McClean, T Monz, H Shen, P Jurcevic, ... Physical Review X 8 (3), 031022, 2018 | 719 | 2018 |
Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz J Romero, R Babbush, JR McClean, C Hempel, PJ Love, A Aspuru-Guzik Quantum Science and Technology 4 (1), 014008, 2018 | 697 | 2018 |
Hartree-Fock on a superconducting qubit quantum computer Google AI Quantum and Collaborators*†, F Arute, K Arya, R Babbush, ... Science 369 (6507), 1084-1089, 2020 | 646 | 2020 |
Power of data in quantum machine learning HY Huang, M Broughton, M Mohseni, R Babbush, S Boixo, H Neven, ... Nature communications 12 (1), 1-9, 2021 | 636 | 2021 |
Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states JR McClean, ME Kimchi-Schwartz, J Carter, WA De Jong Physical Review A 95 (4), 042308, 2017 | 563 | 2017 |
Computation of molecular spectra on a quantum processor with an error-resilient algorithm JI Colless, VV Ramasesh, D Dahlen, MS Blok, ME Kimchi-Schwartz, ... Physical Review X 8 (1), 011021, 2018 | 557 | 2018 |
Quantum approximate optimization of non-planar graph problems on a planar superconducting processor MP Harrigan, KJ Sung, M Neeley, KJ Satzinger, F Arute, K Arya, J Atalaya, ... Nature Physics 17 (3), 332-336, 2021 | 523 | 2021 |
OpenFermion: the electronic structure package for quantum computers JR McClean, NC Rubin, KJ Sung, ID Kivlichan, X Bonet-Monroig, Y Cao, ... Quantum Science and Technology 5 (3), 034014, 2020 | 499 | 2020 |
Quantum simulation of electronic structure with linear depth and connectivity ID Kivlichan, J McClean, N Wiebe, C Gidney, A Aspuru-Guzik, GKL Chan, ... Physical review letters 120 (11), 110501, 2018 | 430 | 2018 |
Suppressing quantum errors by scaling a surface code logical qubit Nature 614 (7949), 676-681, 2023 | 420 | 2023 |
Quantum advantage in learning from experiments HY Huang, M Broughton, J Cotler, S Chen, J Li, M Mohseni, H Neven, ... Science 376 (6598), 1182-1186, 2022 | 410 | 2022 |
Encoding electronic spectra in quantum circuits with linear T complexity R Babbush, C Gidney, DW Berry, N Wiebe, J McClean, A Paler, A Fowler, ... Physical Review X 8 (4), 041015, 2018 | 394 | 2018 |
Tensorflow quantum: A software framework for quantum machine learning M Broughton, G Verdon, T McCourt, AJ Martinez, JH Yoo, SV Isakov, ... arXiv preprint arXiv:2003.02989, 2020 | 378 | 2020 |
Low-depth quantum simulation of materials R Babbush, N Wiebe, J McClean, J McClain, H Neven, GKL Chan Physical Review X 8 (1), 011044, 2018 | 377 | 2018 |