The ITensor Software Library for Tensor Network Calculations
Impact in
Classified as
- Journal
- arXiv (Cornell University)
In The Last Decade
doi.org/10.21468/scipostphyscodeb.4 →Countries where authors are citing The ITensor Software Library for Tensor Network Calculations
This map shows the geographic impact of The ITensor Software Library for Tensor Network Calculations. 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 The ITensor Software Library for Tensor Network Calculations with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The ITensor Software Library for Tensor Network Calculations more than expected).
Fields of papers citing The ITensor Software Library for Tensor Network Calculations
This network shows the impact of The ITensor Software Library for Tensor Network Calculations. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The ITensor Software Library for Tensor Network Calculations.
About The ITensor Software Library for Tensor Network Calculations
This paper, published in 2022, received 523 indexed citations . Written by Matthew Fishman, Steven R. White and E. Miles Stoudenmire covering the research area of Computational Mathematics and Atomic and Molecular Physics, and Optics. It is primarily cited by scholars working on Atomic and Molecular Physics, and Optics (434 citations), Condensed Matter Physics (204 citations), Artificial Intelligence (121 citations), Statistical and Nonlinear Physics (68 citations) and Nuclear and High Energy Physics (42 citations). Published in arXiv (Cornell University).
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.
This paper is also available at doi.org/10.21468/scipostphyscodeb.4.