Johnnie Gray

992 total citations · 1 hit paper
16 papers, 550 citations indexed

About

Johnnie Gray is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, Johnnie Gray has authored 16 papers receiving a total of 550 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Atomic and Molecular Physics, and Optics, 10 papers in Artificial Intelligence and 7 papers in Statistical and Nonlinear Physics. Recurrent topics in Johnnie Gray's work include Quantum many-body systems (9 papers), Quantum Information and Cryptography (8 papers) and Quantum Computing Algorithms and Architecture (8 papers). Johnnie Gray is often cited by papers focused on Quantum many-body systems (9 papers), Quantum Information and Cryptography (8 papers) and Quantum Computing Algorithms and Architecture (8 papers). Johnnie Gray collaborates with scholars based in United States, United Kingdom and China. Johnnie Gray's co-authors include Garnet Kin‐Lic Chan, Abolfazl Bayat, Sougato Bose, Daniel G. A. Smith, Leonardo Banchi, Tomislav Begušić, Reza Haghshenas, Andrew C. Potter, Phillip Helms and Alexander M. Dalzell and has published in prestigious journals such as Physical Review Letters, Nature Communications and Science Advances.

In The Last Decade

Johnnie Gray

15 papers receiving 526 citations

Hit Papers

align trajectories

What are hit papers?

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.

Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry

2023 143 citations Seunghoon Lee, Joonho Lee et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Johnnie Gray United States	9	406	365	78	55	45	16	550
Jin-Guo Liu China	8	285 0.7×	338 0.9×	51 0.7×	97 1.8×	47 1.0×	14	530
Shi-Ju Ran China	15	581 1.4×	301 0.8×	90 1.2×	282 5.1×	82 1.8×	48	772
Andrew J. Ferris Australia	13	441 1.1×	319 0.9×	47 0.6×	54 1.0×	31 0.7×	19	494
Christopher T. Chubb Australia	7	214 0.5×	192 0.5×	108 1.4×	35 0.6×	47 1.0×	13	311
Sonika Johri United States	17	564 1.4×	470 1.3×	99 1.3×	99 1.8×	7 0.2×	31	770
Erika Ye United States	6	318 0.8×	381 1.0×	47 0.6×	14 0.3×	21 0.5×	9	486
Fengping Jin Germany	16	452 1.1×	375 1.0×	148 1.9×	79 1.4×	6 0.1×	49	697
Piotr Czarnik Poland	17	625 1.5×	373 1.0×	77 1.0×	355 6.5×	31 0.7×	24	826
Artur García-Sáez Spain	14	303 0.7×	294 0.8×	49 0.6×	61 1.1×	12 0.3×	30	447

Countries citing papers authored by Johnnie Gray

Since Specialization

Citations

This map shows the geographic impact of Johnnie Gray'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 Johnnie Gray with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johnnie Gray more than expected).

Fields of papers citing papers by Johnnie Gray

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johnnie Gray. 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 Johnnie Gray. The network helps show where Johnnie Gray may publish in the future.

Co-authorship network of co-authors of Johnnie Gray

This figure shows the co-authorship network connecting the top 25 collaborators of Johnnie Gray. A scholar is included among the top collaborators of Johnnie Gray 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 Johnnie Gray. Johnnie Gray is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Gray, Johnnie & Garnet Kin‐Lic Chan. (2024). Hyperoptimized Approximate Contraction of Tensor Networks with Arbitrary Geometry. Physical Review X. 14(1). 16 indexed citations

Gray, Johnnie, et al.. (2024). Hyperoptimized approximate contraction of tensor networks for rugged-energy-landscape spin glasses on periodic square and cubic lattices. Physical review. E. 110(6). 65306–65306. 1 indexed citations

Begušić, Tomislav, Johnnie Gray, & Garnet Kin‐Lic Chan. (2024). Fast and converged classical simulations of evidence for the utility of quantum computing before fault tolerance. Science Advances. 10(3). eadk4321–eadk4321. 42 indexed citations

Liu, Wen-Yuan, et al.. (2024). Tensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States. Physical Review Letters. 133(26). 260404–260404. 2 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 →

Gray, Johnnie, et al.. (2023). Arithmetic circuit tensor networks, multivariable function representation, and high-dimensional integration. Physical Review Research. 5(1). 2 indexed citations

Haghshenas, Reza, Johnnie Gray, Andrew C. Potter, & Garnet Kin‐Lic Chan. (2022). Variational Power of Quantum Circuit Tensor Networks. Physical Review X. 12(1). 53 indexed citations

Helms, Phillip, Minseong Lee, Chenghan Li, et al.. (2022). Using Hyperoptimized Tensor Networks and First-Principles Electronic Structure to Simulate the Experimental Properties of the Giant {Mn₈₄} Torus. The Journal of Physical Chemistry Letters. 13(10). 2365–2370. 6 indexed citations

Kwon, Hyukjoon, et al.. (2020). Quantum Delocalized Interactions. Physical Review Letters. 125(24). 240406–240406.

10.

Gray, Johnnie, et al.. (2020). Efficient Approximate Quantum State Tomography with Basis Dependent Neural-Networks.. 1 indexed citations

11.

Gray, Johnnie. (2018). quimb: A python package for quantum information and many-body calculations. The Journal of Open Source Software. 3(29). 819–819. 90 indexed citations

12.

Gray, Johnnie, Leonardo Banchi, Abolfazl Bayat, & Sougato Bose. (2018). Machine-Learning-Assisted Many-Body Entanglement Measurement. Physical Review Letters. 121(15). 150503–150503. 74 indexed citations

13.

Gray, Johnnie, Sougato Bose, & Abolfazl Bayat. (2018). Many-body localization transition: Schmidt gap, entanglement length, and scaling. Physical review. B.. 97(20). 56 indexed citations

14.

Smith, Daniel G. A. & Johnnie Gray. (2018). opt\_einsum - A Python package for optimizing contraction order for einsum-like expressions. The Journal of Open Source Software. 3(26). 753–753. 52 indexed citations

15.

Gray, Johnnie, Leonardo Banchi, Abolfazl Bayat, & Sougato Bose. (2017). Measuring Entanglement Negativity. 4 indexed citations

16.

Gray, Johnnie, Abolfazl Bayat, Reuben K. Puddy, Charles G. Smith, & Sougato Bose. (2016). Unravelling quantum dot array simulators via singlet-triplet measurements. Physical review. B.. 94(19). 8 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.

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