| 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 | 8820 | 2019 |
| Variational Quantum Algorithms M Cerezo, A Arrasmith, R Babbush, SC Benjamin, S Endo, K Fujii, ... Nature Reviews Physics, 1-20, 2021 | 2753 | 2021 |
| The Theory of Variational Hybrid Quantum-Classical Algorithms J McClean, J Romero, R Babbush, A Aspuru-Guzik New Journal of Physics 18 (2), 023023, 2016 | 2356 | 2016 |
| Barren Plateaus in Quantum Neural Network Training Landscapes JR McClean, S Boixo, VN Smelyanskiy, R Babbush, H Neven Nature Communications 9 (1), 4812, 2018 | 2089 | 2018 |
| Characterizing Quantum Supremacy in Near-Term Devices S Boixo, S Isakov, V Smelyanskiy, R Babbush, N Ding, Z Jiang, J Martinis, ... Nature Physics 14 (6), 595–600, 2018 | 1433 | 2018 |
| Scalable Quantum Simulation of Molecular Energies P O'Malley, R Babbush, I Kivlichan, J Romero, J McClean, R Barends, ... Physical Review X 6 (3), 031007, 2016 | 1313 | 2016 |
| Hartree-Fock on a Superconducting Qubit Quantum Computer GAI Quantum Science 369 (6507), 1084-1089, 2020 | 856 | 2020 |
| 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 | 774 | 2018 |
| Strategies for Quantum Computing Molecular Energies Using the Unitary Coupled Cluster Ansatz J Romero, R Babbush, J McClean, C Hempel, P Love, A Aspuru-Guzik Quantum Science and Technology 4 (1), 14008, 2018 | 761 | 2018 |
| Power of Data in Quantum Machine Learning HY Huang, M Broughton, M Mohseni, R Babbush, S Boixo, H Neven, ... Nature Communications 12 (2631), 2020 | 744 | 2020 |
| What is the Computational Value of Finite Range Tunneling? V Denchev, S Boixo, S Isakov, N Ding, R Babbush, V Smelyanskiy, ... Physical Review X 6 (3), 031015, 2016 | 589 | 2016 |
| 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, 1-5, 2021 | 586 | 2021 |
| Suppressing Quantum Errors by Scaling a Surface Code Logical Qubit R Acharya, I Aleiner, R Allen, TI Andersen, M Ansmann, F Arute, K Arya, ... Nature 614, 676–681, 2023 | 565 | 2023 |
| Digitized Adiabatic Quantum Computing with a Superconducting Circuit R Barends, A Shabani, L Lamata, J Kelly, A Mezzacapo, U Las Heras, ... Nature 534 (7606), 222-226, 2016 | 559 | 2016 |
| OpenFermion: The Electronic Structure Package for Quantum Computers J McClean, N Rubin, K Sung, ID Kivlichan, X Bonet-Monroig, Y Cao, ... Quantum Science and Technology, 2020 | 551 | 2020 |
| 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 | 511 | 2022 |
| Quantum Simulation of Electronic Structure with Linear Depth and Connectivity ID Kivlichan, J McClean, N Wiebe, C Gidney, A Aspuru-Guzik, GK Chan, ... Physical Review Letters 120 (11), 110501, 2018 | 464 | 2018 |
| Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity R Babbush, C Gidney, D Berry, N Wiebe, J McClean, A Paler, A Fowler, ... Physical Review X 8 (4), 041015, 2018 | 451 | 2018 |
| 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 | 442 | 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 | 417 | 2020 |
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