Two-qubit silicon quantum processor with operation fidelity exceeding 99%

196 indexed citations

Abstract

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This paper, published in 2022, received 196 indexed citations. Written by Adam Mills, Michael J. Gullans, A. J. Sigillito, Mayer M. Feldman, Erik Nielsen and J. R. Petta covering the research area of Artificial Intelligence, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. It is primarily cited by scholars working on Atomic and Molecular Physics, and Optics (174 citations), Artificial Intelligence (106 citations) and Electrical and Electronic Engineering (104 citations). Published in Science Advances.

Countries where authors are citing Two-qubit silicon quantum processor with operation fidelity exceeding 99%

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This map shows the geographic impact of Two-qubit silicon quantum processor with operation fidelity exceeding 99%. 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 Two-qubit silicon quantum processor with operation fidelity exceeding 99% with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Two-qubit silicon quantum processor with operation fidelity exceeding 99% more than expected).

Fields of papers citing Two-qubit silicon quantum processor with operation fidelity exceeding 99%

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Two-qubit silicon quantum processor with operation fidelity exceeding 99%. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Two-qubit silicon quantum processor with operation fidelity exceeding 99%.

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/10.1126/sciadv.abn5130.

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