Ryan Babbush

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 29 papers; trusts 58 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 633 citations into it; #4,200 of 35,345 by trust here, #1,148 by citations
• Atomic and Molecular Physics, and Optics — 446 citations into it; #390 of 7,135 by trust here, #174 by citations
• Computational Theory and Mathematics — 139 citations into it
• Electrical and Electronic Engineering — 56 citations into it
• Physical and Theoretical Chemistry — 34 citations into it
• Statistical and Nonlinear Physics — 19 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Numerical Analysis — 12 citations into it

Whom Ryan Babbush trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jarrod R. McClean
2. Dominic W. Berry
3. Sergey Bravyi
4. Guang Hao Low
5. Nathan Wiebe
6. Andrew M. Childs
7. Edward Farhi
8. Jonathan Romero
9. Dave Wecker
10. A. Aspuru‐Guzik

Who trusts Ryan Babbush overall

1. Yudong Cao
2. Nathan Wiebe
3. Göran Wendin
4. Jonathan Romero
5. Jarrod R. McClean
6. M. Cerezo
7. Daniel Litinski
8. Alán Aspuru-Guzik
9. Craig Gidney
10. Guang Hao Low

Papers from Ryan Babbush's perspective

Ordered by how strongly the researchers Ryan Babbush trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Quantum Computation and Quantum Information
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. I and J

Their papers

• Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator (2018) — 547 citations
• Scalable Quantum Simulation of Molecular Energies (2015) — 1,013 citations
• Variational quantum algorithms (2020) — 3,982 citations
• Quantum approximate optimization of non-planar graph problems on a planar superconducting processor (2020) — 622 citations
• Suppressing quantum errors by scaling a surface code logical qubit (2022) — 1,354 citations
• Exponential suppression of bit or phase errors with cyclic error correction (2021) — 443 citations
• Quantum error mitigation (2022) — 712 citations
• Matchgate Shadows for Fermionic Quantum Simulation (2022) — 135 citations
• Quantum advantage in learning from experiments (2021) — 690 citations
• Hartree-Fock on a superconducting qubit quantum computer (2020) — 849 citations
• Power of data in quantum machine learning (2020) — 1,021 citations
• Observation of separated dynamics of charge and spin in the Fermi-Hubbard model (2020) — 165 citations
• Even More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction (2020) — 338 citations
• Quantum supremacy using a programmable superconducting processor (2019) — 7,893 citations
• Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers (2019) — 300 citations
• Barren plateaus in quantum neural network training landscapes (2018) — 2,812 citations
• Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity (2018) — 484 citations
• Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz (2017) — 768 citations
• Low-Depth Quantum Simulation of Materials (2017) — 345 citations
• Improved techniques for preparing eigenstates of fermionic Hamiltonians (2017) — 159 citations