Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The theory of variational hybrid quantum-classical algorithms
20161.3k citationsJarrod R. McClean, Ryan Babbush et al.profile →
Barren plateaus in quantum neural network training landscapes
20181.2k citationsJarrod R. McClean, Sergio Boixo et al.Nature Communicationsprofile →
Characterizing quantum supremacy in near-term devices
2018628 citationsSergio Boixo, Ryan Babbush et al.Nature Physicsprofile →
Quantum advantage in learning from experiments
2022298 citationsHsin-Yuan Huang, Michael Broughton et al.Scienceprofile →
Quantum error mitigation
2023260 citationsRyan Babbush, William J. Huggins et al.profile →
Even More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction
2021189 citationsJoonho Lee, Dominic W. Berry et al.PRX Quantumprofile →
Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry
2023143 citationsSeunghoon Lee, Joonho Lee et al.Nature Communicationsprofile →
Unbiasing fermionic quantum Monte Carlo with a quantum computer
2022142 citationsWilliam J. Huggins, Bryan O’Gorman et al.Natureprofile →
Commercialize quantum technologies in five years
2017137 citationsMasoud Mohseni, Hartmut Neven et al.Natureprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of Ryan Babbush's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ryan Babbush with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan Babbush more than expected).
This network shows the impact of papers produced by Ryan Babbush. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ryan Babbush. The network helps show where Ryan Babbush may publish in the future.
Co-authorship network of co-authors of Ryan Babbush
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Babbush.
A scholar is included among the top collaborators of Ryan Babbush based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ryan Babbush. Ryan Babbush is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Santagati, Raffaele, Alán Aspuru‐Guzik, Ryan Babbush, et al.. (2024). Drug design on quantum computers. Nature Physics. 20(4). 549–557.47 indexed citations
Lee, Seunghoon, Joonho Lee, Huanchen Zhai, et al.. (2023). Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry. Nature Communications. 14(1). 1952–1952.143 indexed citations breakdown →
10.
Huang, Hsin-Yuan, Michael Broughton, Jordan Cotler, et al.. (2022). Quantum advantage in learning from experiments. Science. 376(6598). 1182–1186.298 indexed citations breakdown →
11.
Huggins, William J., Bryan O’Gorman, Nicholas C. Rubin, et al.. (2022). Unbiasing fermionic quantum Monte Carlo with a quantum computer. Nature. 603(7901). 416–420.142 indexed citations breakdown →
12.
Sung, Kevin J., Matthew P. Harrigan, Nicholas C. Rubin, et al.. (2020). An Exploration of Practical Optimizers for Variational Quantum Algorithms on Superconducting Qubit Processors. arXiv (Cornell University).2 indexed citations
Berry, Dominic W., Craig Gidney, Mário Motta, Jarrod R. McClean, & Ryan Babbush. (2019). Qubitization of Arbitrary Basis Quantum Chemistry by Low Rank Factorization. arXiv (Cornell University).3 indexed citations
15.
Lavrijsen, W., Jeffrey Larson, Kevin J. Sung, et al.. (2019). SKQuant-Opt: Optimizers for Noisy Intermediate-Scale Quantum Devices. Bulletin of the American Physical Society. 2019.1 indexed citations
16.
Ding, Nan, et al.. (2014). Bayesian Sampling Using Stochastic Gradient Thermostats. Neural Information Processing Systems. 27. 3203–3211.61 indexed citations
Cao, Yudong, Ryan Babbush, Jacob Biamonte, & Sabre Kais. (2013). Experimentally Realizable Hamiltonian Gadgets. arXiv (Cornell University).2 indexed citations
19.
Cao, Yudong, Ryan Babbush, Jacob Biamonte, & Sabre Kais. (2013). Improved Hamiltonian gadgets. arXiv (Cornell University).1 indexed citations
20.
Babbush, Ryan, Alejandro Perdomo‐Ortiz, Bryan O’Gorman, William G. Macready, & Alán Aspuru‐Guzik. (2012). Construction of Energy Functions for Lattice Heteropolymer Models: Efficient Encodings for Constraint Satisfaction Programming and Quantum Annealing. arXiv (Cornell University).2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.