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.
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
20171.7k citationsAbhinav Kandala, Antonio Mezzacapo et al.Natureprofile →
Supervised learning with quantum-enhanced feature spaces
20191.2k citationsAntonio Córcoles, Kristan Temme et al.Natureprofile →
Coupling superconducting qubits via a cavity bus
2007960 citationsJohannes Majer, Jerry M. Chow et al.Natureprofile →
Demonstration of two-qubit algorithms with a superconducting quantum processor
2009798 citationsL. DiCarlo, Jerry M. Chow et al.Natureprofile →
Error mitigation extends the computational reach of a noisy quantum processor
2019502 citationsAbhinav Kandala, Kristan Temme et al.Natureprofile →
Preparation and measurement of three-qubit entanglement in a superconducting circuit
2010417 citationsL. DiCarlo, Matthew D. Reed et al.Natureprofile →
Superconducting qubit in a waveguide cavity with a coherence time approaching 0.1 ms
2012380 citationsChad Rigetti, Jay Gambetta et al.profile →
Suppressing charge noise decoherence in superconducting charge qubits
2008374 citationsJ. A. Schreier, Andrew Houck et al.profile →
Generating single microwave photons in a circuit
2007336 citationsAndrew Houck, David Schuster et al.Natureprofile →
Efficient Z gates for quantum computing
2017333 citationsDavid McKay, Sarah Sheldon et al.profile →
Controlling the Spontaneous Emission of a Superconducting Transmon Qubit
2008314 citationsAndrew Houck, J. A. Schreier et al.Physical Review Lettersprofile →
Building logical qubits in a superconducting quantum computing system
2017308 citationsJay Gambetta, Jerry M. Chow et al.profile →
Demonstration of a quantum error detection code using a square lattice of four superconducting qubits
2015301 citationsAntonio Córcoles, Easwar Magesan et al.Nature Communicationsprofile →
Procedure for systematically tuning up cross-talk in the cross-resonance gate
2016276 citationsSarah Sheldon, Easwar Magesan et al.profile →
Nonlinear response of the vacuum Rabi resonance
2008202 citationsLev S. Bishop, Jerry M. Chow et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Jerry M. Chow'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 Jerry M. Chow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jerry M. Chow more than expected).
This network shows the impact of papers produced by Jerry M. Chow. 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 Jerry M. Chow. The network helps show where Jerry M. Chow may publish in the future.
Co-authorship network of co-authors of Jerry M. Chow
This figure shows the co-authorship network connecting the top 25 collaborators of Jerry M. Chow.
A scholar is included among the top collaborators of Jerry M. Chow 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 Jerry M. Chow. Jerry M. Chow is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hertzberg, Jared, Sami Rosenblatt, Easwar Magesan, et al.. (2020). Effects of qubit frequency crowding on scalable quantum processors. Bulletin of the American Physical Society.1 indexed citations
3.
Rosenblatt, Sami, Nicholas T. Bronn, Hanhee Paik, et al.. (2019). Enablement of near-term quantum processors by architectural yield engineering. Bulletin of the American Physical Society. 2019.1 indexed citations
4.
Sandberg, Martin, Markus Brink, Vivekananda P. Adiga, et al.. (2019). Low temperature measurement of SiGe properties for superconducting quantum circuits. Bulletin of the American Physical Society. 2019.1 indexed citations
5.
McKay, David, Sarah Sheldon, John A. Smolin, Jerry M. Chow, & Jay Gambetta. (2019). Three-Qubit Randomized Benchmarking. Physical Review Letters. 122(20). 200502–200502.90 indexed citations
6.
Kandala, Abhinav, Antonio Mezzacapo, Kristan Temme, et al.. (2017). Hardware-efficient Quantum Optimizer for Small Molecules and Quantum Magnets. arXiv (Cornell University).8 indexed citations
7.
Kandala, Abhinav, Antonio Mezzacapo, Kristan Temme, et al.. (2017). Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature. 549(7671). 242–246.1720 indexed citations breakdown →
Chow, Jerry M., Antonio Córcoles, Jay Gambetta, et al.. (2012). High-fidelity gates towards a scalable superconducting quantum processor. Bulletin of the American Physical Society. 2012.2 indexed citations
DiCarlo, L., Matthew D. Reed, Luyan Sun, et al.. (2010). Preparation and measurement of three-qubit entanglement in a superconducting circuit. Nature. 467(7315). 574–578.417 indexed citations breakdown →
DiCarlo, L., Jerry M. Chow, Jay Gambetta, et al.. (2009). Demonstration of two-qubit algorithms with a superconducting quantum processor. Nature. 460(7252). 240–244.798 indexed citations breakdown →
19.
Houck, Andrew, J. A. Schreier, Blake Johnson, et al.. (2008). Controlling the Spontaneous Emission of a Superconducting Transmon Qubit. Physical Review Letters. 101(8). 80502–80502.314 indexed citations breakdown →
20.
Majer, Johannes, Jerry M. Chow, Jay Gambetta, et al.. (2007). Coupling superconducting qubits via a cavity bus. Nature. 449(7161). 443–447.960 indexed citations breakdown →
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.