Raoul Heese

1.2k total citations · 1 hit paper
20 papers, 589 citations indexed

About

Raoul Heese is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Control and Systems Engineering. According to data from OpenAlex, Raoul Heese has authored 20 papers receiving a total of 589 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 7 papers in Computational Theory and Mathematics and 4 papers in Control and Systems Engineering. Recurrent topics in Raoul Heese's work include Quantum Computing Algorithms and Architecture (8 papers), Quantum Information and Cryptography (7 papers) and Quantum Mechanics and Applications (4 papers). Raoul Heese is often cited by papers focused on Quantum Computing Algorithms and Architecture (8 papers), Quantum Information and Cryptography (7 papers) and Quantum Mechanics and Applications (4 papers). Raoul Heese collaborates with scholars based in Germany, Finland and United Kingdom. Raoul Heese's co-authors include Christian Bauckhage, Bogdan Georgiev, Sebastian Mayer, Birgit Kirsch, Katharina Beckh, Jannis Schuecker, Rajkumar Ramamurthy, Michał Walczak, Jochen Garcke and Sven Giesselbach and has published in prestigious journals such as PLoS ONE, Scientific Reports and European Journal of Operational Research.

In The Last Decade

Raoul Heese

19 papers receiving 562 citations

Hit Papers

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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.

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

2021 469 citations Laura von Rueden, Sebastian Mayer et al. IEEE Transactions on Knowledge and Data Engineering profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Raoul Heese	239	94	63	57	49	20	589
Sebastian Mayer	190 0.8×	73 0.8×	88 1.4×	83 1.5×	47 1.0×	23	677
Bogdan Georgiev	186 0.8×	69 0.7×	58 0.9×	50 0.9×	46 0.9×	8	483
Rajkumar Ramamurthy	231 1.0×	68 0.7×	57 0.9×	50 0.9×	44 0.9×	13	537
Sven Giesselbach	201 0.8×	70 0.7×	57 0.9×	55 1.0×	46 0.9×	10	544
Laura von Rueden	187 0.8×	70 0.7×	57 0.9×	51 0.9×	45 0.9×	5	485
Katharina Beckh	188 0.8×	70 0.7×	56 0.9×	51 0.9×	44 0.9×	8	493
Birgit Kirsch	183 0.8×	68 0.7×	56 0.9×	50 0.9×	49 1.0×	3	519
Jannis Schuecker	189 0.8×	69 0.7×	80 1.3×	50 0.9×	75 1.5×	7	548
Yuchen Wu	199 0.8×	46 0.5×	82 1.3×	86 1.5×	19 0.4×	12	664
Matthias Humt	213 0.9×	99 1.1×	44 0.7×	33 0.6×	21 0.4×	11	591

Countries citing papers authored by Raoul Heese

Since Specialization

Citations

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

Fields of papers citing papers by Raoul Heese

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Raoul Heese

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

All Works

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20 of 20 papers shown

Heese, Raoul, et al.. (2025). Quantum computing for discrete optimization: A highlight of three technologies. European Journal of Operational Research. 329(3). 747–766.

Heese, Raoul, et al.. (2025). Explaining quantum circuits with Shapley values: towards explainable quantum machine learning. Quantum Machine Intelligence. 7(1). 1 indexed citations

Seidel, Tobias, et al.. (2025). Efficient solution of the number partitioning problem on a quantum annealer: a hybrid quantum-classical decomposition approach. Journal of Heuristics. 31(2). 1 indexed citations

Heese, Raoul. (2024). Quantum Wave Function Collapse for Procedural Content Generation. IEEE Computer Graphics and Applications. 44(5). 54–66. 1 indexed citations

Heese, Raoul, et al.. (2024). On the effects of biased quantum random numbers on the initialization of artificial neural networks. Machine Learning. 113(3). 1189–1217. 3 indexed citations

Heese, Raoul, et al.. (2024). Adaptively exploring the feature space of flowsheets. AIChE Journal. 70(7). 2 indexed citations

Heese, Raoul, et al.. (2023). Feature selection on quantum computers. Quantum Machine Intelligence. 5(1). 28 indexed citations

Seidel, Tobias, et al.. (2023). Multiplicities in thermodynamic activity coefficients. AIChE Journal. 69(12). 8 indexed citations

Seidel, Tobias, et al.. (2023). An optimization case study for solving a transport robot scheduling problem on quantum-hybrid and quantum-inspired hardware. Scientific Reports. 13(1). 18743–18743. 6 indexed citations

10.

Heese, Raoul, Jochen Schmid, Michał Walczak, & Michael Bortz. (2023). Calibrated simplex-mapping classification. PLoS ONE. 18(1). e0279876–e0279876. 1 indexed citations

11.

Heese, Raoul, et al.. (2023). Fast and Non-Invasive Evaluation of Yeast Viability in Fermentation Processes Using Raman Spectroscopy and Machine Learning. Beverages. 9(3). 68–68. 4 indexed citations

12.

Heese, Raoul, et al.. (2022). The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach. Frontiers in Aging Neuroscience. 14. 866886–866886. 4 indexed citations

13.

Georgiev, Bogdan, et al.. (2022). Quantum Circuit Evolution on NISQ Devices. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1–8. 8 indexed citations

14.

Heese, Raoul, et al.. (2022). Representation of binary classification trees with binary features by quantum circuits. Quantum. 6. 676–676. 16 indexed citations

15.

Rueden, Laura von, Sebastian Mayer, Katharina Beckh, et al.. (2021). Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. IEEE Transactions on Knowledge and Data Engineering. 1–1. 469 indexed citations breakdown →

16.

Ludl, Patrick Otto, et al.. (2021). Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet simulations with constraints. Frontiers of Chemical Science and Engineering. 16(2). 183–197. 13 indexed citations

17.

Heese, Raoul, et al.. (2020). Some Aspects of Combining Data and Models in Process Engineering. Chemie Ingenieur Technik. 92(7). 856–866. 1 indexed citations

18.

Heese, Raoul, et al.. (2019). Optimized data exploration applied to the simulation of a chemical process. Computers & Chemical Engineering. 124. 326–342. 17 indexed citations

19.

Heese, Raoul & Matthias Freyberger. (2014). Pointer-based simultaneous measurements of conjugate observables in a thermal environment. Physical Review A. 89(5). 3 indexed citations

20.

Heese, Raoul & Matthias Freyberger. (2013). Entropic uncertainty relation for pointer-based simultaneous measurements of conjugate observables. Physical Review A. 87(1). 3 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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