J. Casanova

2.9k total citations · 1 hit paper
67 papers, 2.0k citations indexed

About

J. Casanova is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Materials Chemistry. According to data from OpenAlex, J. Casanova has authored 67 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Atomic and Molecular Physics, and Optics, 38 papers in Artificial Intelligence and 22 papers in Materials Chemistry. Recurrent topics in J. Casanova's work include Quantum Information and Cryptography (38 papers), Quantum Computing Algorithms and Architecture (24 papers) and Diamond and Carbon-based Materials Research (22 papers). J. Casanova is often cited by papers focused on Quantum Information and Cryptography (38 papers), Quantum Computing Algorithms and Architecture (24 papers) and Diamond and Carbon-based Materials Research (22 papers). J. Casanova collaborates with scholars based in Spain, Germany and China. J. Casanova's co-authors include E. Solano, Juan José García‐Ripoll, Martin B. Plenio, Lucas Lamata, G. Romero, I. Lizuain, Antonio Mezzacapo, Ricardo Puebla, C. F. Roos and R. Gerritsma and has published in prestigious journals such as Physical Review Letters, Nature Communications and Scientific Reports.

In The Last Decade

J. Casanova

63 papers receiving 2.0k citations

Hit Papers

Deep Strong Coupling Regime of the Jaynes-Cummings Model 2010 2026 2015 2020 2010 100 200 300
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.

  1. Deep Strong Coupling Regime of the Jaynes-Cummings Model
    2010 399 citations J. Casanova, E. Solano et al. Physical Review Letters profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Casanova Spain 24 1.8k 1.4k 265 147 101 67 2.0k
Diego Ristè United States 16 1.6k 0.9× 1.2k 0.9× 477 1.8× 109 0.7× 222 2.2× 21 2.0k
Fumiki Yoshihara Japan 19 2.1k 1.2× 1.7k 1.3× 142 0.5× 124 0.8× 218 2.2× 37 2.3k
Jonas Bylander Sweden 20 1.5k 0.8× 1.2k 0.8× 141 0.5× 89 0.6× 294 2.9× 48 1.7k
D. J. Wineland United States 13 2.8k 1.6× 2.2k 1.6× 89 0.3× 143 1.0× 159 1.6× 16 3.0k
Dolev Bluvstein United States 15 1.0k 0.6× 641 0.5× 224 0.8× 77 0.5× 116 1.1× 24 1.4k
Fernando Pastawski Germany 16 1.0k 0.6× 819 0.6× 390 1.5× 171 1.2× 212 2.1× 22 1.6k
Monika Schleier-Smith United States 23 2.4k 1.4× 1.2k 0.9× 143 0.5× 293 2.0× 117 1.2× 42 2.6k
Kaveh Khodjasteh United States 14 1.1k 0.6× 917 0.7× 82 0.3× 103 0.7× 102 1.0× 17 1.2k
Yuimaru Kubo Japan 14 1.9k 1.1× 971 0.7× 388 1.5× 61 0.4× 370 3.7× 32 2.1k
Bryan K. Clark United States 20 1.1k 0.6× 583 0.4× 191 0.7× 198 1.3× 76 0.8× 55 1.5k

Countries citing papers authored by J. Casanova

Since Specialization
Citations

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

Fields of papers citing papers by J. Casanova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Casanova

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

All Works

20 of 20 papers shown
1.
Casanova, J., et al.. (2025). Robust microwave cavity control for NV ensemble manipulation. Physical Review Research. 7(1).
2.
Casanova, J., et al.. (2025). Pulse sequence design for high field NMR with NV centers in dipolarly coupled samples. Scientific Reports. 15(1). 30956–30956.
3.
Acedo, Pablo, et al.. (2024). Enhancing polarization transfer from nitrogen-vacancy centers to external nuclear spins via dangling bond mediators. Communications Physics. 7(1). 2 indexed citations
4.
Casanova, J., et al.. (2024). Photon-emission statistics for single nitrogen-vacancy centers. Physical Review Applied. 22(1). 7 indexed citations
5.
Ban, Yue, et al.. (2024). Automatic Detection of Nuclear Spins at Arbitrary Magnetic Fields via Signal-to-Image AI Model. Physical Review Letters. 132(15). 150801–150801.
6.
Casanova, J., et al.. (2024). Versatile Quadrature Antenna for Precise Control of Large Electron Spin Ensembles in Diamond. Advanced Quantum Technologies. 8(3).
7.
Casanova, J., et al.. (2023). High-Resolution NMR Spectroscopy at Large Fields with Nitrogen Vacancy Centers. Physical Review Letters. 130(13). 133603–133603. 15 indexed citations
8.
Casanova, J., et al.. (2023). Robust two-qubit gates using pulsed dynamical decoupling. New Journal of Physics. 25(6). 63023–63023. 6 indexed citations
9.
Puebla, Ricardo, et al.. (2022). Detection of molecular transitions with nitrogen-vacancy centers and electron-spin labels. npj Quantum Information. 8(1). 6 indexed citations
10.
Leib, Martin, et al.. (2022). Co-Design quantum simulation of nanoscale NMR. Physical Review Research. 4(4). 7 indexed citations
11.
Torrontegui, E., et al.. (2022). Tailored ion beam for precise colour centre creation. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 380(2239). 20210271–20210271. 1 indexed citations
12.
Ban, Yue, Jin‐Ming Cui, Yun‐Feng Huang, et al.. (2022). A neural network assisted 171Yb+ quantum magnetometer. npj Quantum Information. 8(1). 10 indexed citations
13.
Ban, Yue, et al.. (2021). Breaking adiabatic quantum control with deep learning. Physical review. A. 103(4). 33 indexed citations
14.
Dong, Lijuan, Íñigo Arrazola, Xi Chen, & J. Casanova. (2021). Phase-Adaptive Dynamical Decoupling Methods for Robust Spin-Spin Dynamics in Trapped Ions. Physical Review Applied. 15(3). 5 indexed citations
15.
Wang, Zhenyu, J. Casanova, & Martin B. Plenio. (2020). Enhancing the Robustness of Dynamical Decoupling Sequences with Correlated Random Phases. Symmetry. 12(5). 730–730. 8 indexed citations
16.
Casanova, J., et al.. (2020). Measurement-Based Adaptation Protocol with Quantum Reinforcement Learning in a Rigetti Quantum Computer. Quantum Reports. 2(2). 293–304. 14 indexed citations
17.
Ban, Yue, et al.. (2020). Robust Detection of High-Frequency Signals at the Nanoscale. Physical Review Applied. 14(5). 9 indexed citations
18.
Zhang, Jing-Ning, Íñigo Arrazola, J. Casanova, et al.. (2020). Probabilistic eigensolver with a trapped-ion quantum processor. Physical review. A. 101(5). 8 indexed citations
19.
Casanova, J., et al.. (2017). Enhanced Resolution in Nanoscale NMR via Quantum Sensing with Pulses of Finite Duration. UCL Discovery (University College London). 17 indexed citations
20.
Zhang, Xiang, Yangchao Shen, Junhua Zhang, et al.. (2015). Time reversal and charge conjugation in an embedding quantum simulator. Nature Communications. 6(1). 7917–7917. 23 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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