Leonard Wossnig

2.1k total citations · 1 hit paper
15 papers, 1.2k citations indexed

About

Leonard Wossnig is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Molecular Biology. According to data from OpenAlex, Leonard Wossnig has authored 15 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 3 papers in Atomic and Molecular Physics, and Optics and 1 paper in Molecular Biology. Recurrent topics in Leonard Wossnig's work include Quantum Computing Algorithms and Architecture (12 papers), Quantum Information and Cryptography (10 papers) and Neural Networks and Reservoir Computing (3 papers). Leonard Wossnig is often cited by papers focused on Quantum Computing Algorithms and Architecture (12 papers), Quantum Information and Cryptography (10 papers) and Neural Networks and Reservoir Computing (3 papers). Leonard Wossnig collaborates with scholars based in United Kingdom, United States and China. Leonard Wossnig's co-authors include Edward Grant, Hongxiang Chen, Simone Severini, Jules Tilly, Shuxiang Cao, Kanav Setia, Ying Li, Ivan Rungger, George H. Booth and Jonathan Tennyson and has published in prestigious journals such as Physical Review Letters, Physics Reports and Drug Discovery Today.

In The Last Decade

Leonard Wossnig

15 papers receiving 1.1k 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.

The Variational Quantum Eigensolver: A review of methods and best practices

2022 552 citations Jules Tilly, Hongxiang Chen et al. Physics Reports profile →

Peers

Leonard Wossnig

AI

AMPO

CTM

EEE

MC

Kunal Sharma United States

Matthias Degroote United States

Guang Hao Low United States

Alba Cervera-Lierta Canada

Vojtěch Havlíček Czechia

Jakob S. Kottmann Germany

Daniel J. Egger Switzerland

Iris Cong United States

Craig Gidney United States

Sukin Sim United States

Kunal Sharma United States

Leonard Wossnig

1.1k ×1.0

AI

460 ×1.0

AMPO

168 ×0.7

CTM

109 ×1.2

EEE

54 ×0.9

MC

Citations per year, relative to Leonard Wossnig Leonard Wossnig (= 1×) peers Kunal Sharma

Countries citing papers authored by Leonard Wossnig

Since Specialization

Citations

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

Fields of papers citing papers by Leonard Wossnig

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonard Wossnig

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

All Works

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15 of 15 papers shown

1.

Wossnig, Leonard, Norbert Furtmann, Andrew Buchanan, Sandeep Kumar, & Victor Greiff. (2024). Best practices for machine learning in antibody discovery and development. Drug Discovery Today. 29(7). 104025–104025. 9 indexed citations

2.

Tilly, Jules, Hongxiang Chen, Shuxiang Cao, et al.. (2022). The Variational Quantum Eigensolver: A review of methods and best practices. Physics Reports. 986. 1–128. 552 indexed citations breakdown →

3.

Wossnig, Leonard. (2021). Intelligent drug discovery. Physics World. 34(5). 39–40. 1 indexed citations

4.

Tilly, Jules, Hongxiang Chen, Shuxiang Cao, et al.. (2021). The Variational Quantum Eigensolver: a review of methods and best practices. arXiv (Cornell University). 6 indexed citations

5.

Tilly, Jules, Glenn Jones, Hongxiang Chen, Leonard Wossnig, & Edward Grant. (2020). Computation of molecular excited states on IBM quantum computers using a discriminative variational quantum eigensolver. Physical review. A. 102(6). 23 indexed citations

6.

Wang, Chunhao & Leonard Wossnig. (2020). A quantum algorithm for simulating non-sparse Hamiltonians. Quantum Information and Computation. 20(7&8). 597–615. 9 indexed citations

7.

Cao, Shuxiang, Leonard Wossnig, Brian Vlastakis, Peter Leek, & Edward Grant. (2020). Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits. Physical review. A. 101(5). 19 indexed citations

8.

Ciliberto, Carlo, Andrea Rocchetto, Alessandro Rudi, & Leonard Wossnig. (2020). Statistical limits of supervised quantum learning. Physical review. A. 102(4). 5 indexed citations

9.

Chen, Hongxiang, Leonard Wossnig, Simone Severini, Hartmut Neven, & Morteza Mohseni. (2020). Universal discriminative quantum neural networks. Quantum Machine Intelligence. 3(1). 52 indexed citations

10.

Wossnig, Leonard & Simone Severini. (2019). Quantum machine learning: Challenges and Opportunities. Bulletin of the American Physical Society. 2019. 1 indexed citations

11.

Benedetti, Marcello, Edward Grant, Leonard Wossnig, & Simone Severini. (2019). Adversarial quantum circuit learning for pure state approximation. New Journal of Physics. 21(4). 43023–43023. 50 indexed citations

12.

Cao, Shuxiang, Leonard Wossnig, Brian Vlastakis, Peter Leek, & Edward Grant. (2019). Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits. arXiv (Cornell University). 1 indexed citations

13.

Wossnig, Leonard, Zhikuan Zhao, & Anupam Prakash. (2018). Quantum Linear System Algorithm for Dense Matrices. Physical Review Letters. 120(5). 50502–50502. 143 indexed citations

14.

Ciliberto, Carlo, Mark Herbster, Massimiliano Pontil, et al.. (2018). Quantum machine learning: a classical perspective. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 474(2209). 20170551–20170551. 279 indexed citations

15.

Wossnig, Leonard, et al.. (2016). The Role of Information in Group Formation. 231–235. 1 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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