Bogna Bylicka

787 total citations
10 papers, 542 citations indexed

About

Bogna Bylicka is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, Bogna Bylicka has authored 10 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Atomic and Molecular Physics, and Optics, 9 papers in Artificial Intelligence and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Bogna Bylicka's work include Quantum Information and Cryptography (9 papers), Quantum Computing Algorithms and Architecture (7 papers) and Quantum Mechanics and Applications (7 papers). Bogna Bylicka is often cited by papers focused on Quantum Information and Cryptography (9 papers), Quantum Computing Algorithms and Architecture (7 papers) and Quantum Mechanics and Applications (7 papers). Bogna Bylicka collaborates with scholars based in Poland, Spain and Finland. Bogna Bylicka's co-authors include Dariusz Chruściński, Sabrina Maniscalco, Carole Addis, Antonio Acín, Markus Johansson, Jan Kołodyński, Jonatan Bohr Brask, Martí Perarnau-Llobet, Nadja K. Bernardes and Eugene Liu and has published in prestigious journals such as Physical Review Letters, Scientific Reports and Physical Review A.

In The Last Decade

Bogna Bylicka

10 papers receiving 534 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bogna Bylicka Poland	7	488	486	146	10	7	10	542
A. El Allati Morocco	15	504 1.0×	523 1.1×	114 0.8×	17 1.7×	8 1.1×	78	590
Thao P. Le United Kingdom	7	273 0.6×	259 0.5×	171 1.2×	16 1.6×	5 0.7×	12	335
Areeya Chantasri Australia	10	321 0.7×	270 0.6×	139 1.0×	10 1.0×	5 0.7×	20	348
Michael J. Kewming Australia	10	319 0.7×	307 0.6×	128 0.9×	15 1.5×	9 1.3×	17	418
Nadja K. Bernardes Brazil	11	309 0.6×	322 0.7×	116 0.8×	12 1.2×	5 0.7×	22	375
O. Jiménez Farías Brazil	11	395 0.8×	403 0.8×	76 0.5×	21 2.1×	4 0.6×	20	437
Kimmo Luoma Germany	11	327 0.7×	287 0.6×	117 0.8×	14 1.4×	2 0.3×	31	364
Hongting Song China	8	393 0.8×	399 0.8×	107 0.7×	9 0.9×	5 0.7×	15	428
Elisa Bäumer Switzerland	7	299 0.6×	285 0.6×	142 1.0×	7 0.7×	5 0.7×	9	359
Kok Chuan Tan South Korea	11	552 1.1×	552 1.1×	71 0.5×	19 1.9×	6 0.9×	21	603

Countries citing papers authored by Bogna Bylicka

Since Specialization

Citations

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

Fields of papers citing papers by Bogna Bylicka

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bogna Bylicka

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

All Works

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10 of 10 papers shown

1.

Bylicka, Bogna, et al.. (2023). How to Train an Accurate and Efficient Object Detection Model on any Dataset. 770–778. 1 indexed citations

2.

Johansson, Markus, et al.. (2020). Witnessing non-Markovian dynamics through correlations. Physical review. A. 102(1). 10 indexed citations

3.

Johansson, Markus, et al.. (2019). Correlation measure detecting almost all non-Markovian evolutions. Physical review. A. 99(1). 19 indexed citations

4.

Kołodyński, Jan, Jonatan Bohr Brask, Martí Perarnau-Llobet, & Bogna Bylicka. (2018). Adding dynamical generators in quantum master equations. Physical review. A. 97(6). 33 indexed citations

5.

Bylicka, Bogna, Markus Johansson, & Antonio Acín. (2017). Constructive Method for Detecting the Information Backflow of Non-Markovian Dynamics. Physical Review Letters. 118(12). 120501–120501. 55 indexed citations

6.

Addis, Carole, Bogna Bylicka, Dariusz Chruściński, & Sabrina Maniscalco. (2014). What we talk about when we talk about non-Markovianity. arXiv (Cornell University). 1 indexed citations

7.

Bylicka, Bogna, Dariusz Chruściński, & Sabrina Maniscalco. (2014). Non-Markovianity and reservoir memory of quantum channels: a quantum information theory perspective. Scientific Reports. 4(1). 5720–5720. 235 indexed citations

8.

Addis, Carole, Bogna Bylicka, Dariusz Chruściński, & Sabrina Maniscalco. (2014). Comparative study of non-Markovianity measures in exactly solvable one- and two-qubit models. Physical Review A. 90(5). 112 indexed citations

9.

Bylicka, Bogna & Dariusz Chruściński. (2012). Circulant States with Vanishing Quantum Discord. Open Systems & Information Dynamics. 19(1). 1250006–1250006. 6 indexed citations

10.

Bylicka, Bogna & Dariusz Chruściński. (2010). Witnessing quantum discord in2×Nsystems. Physical Review A. 81(6). 70 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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