S. Vallecorsa

85.6k total citations
58 papers, 569 citations indexed

About

S. Vallecorsa is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, S. Vallecorsa has authored 58 papers receiving a total of 569 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Artificial Intelligence, 19 papers in Nuclear and High Energy Physics and 13 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in S. Vallecorsa's work include Quantum Computing Algorithms and Architecture (30 papers), Quantum Information and Cryptography (17 papers) and Particle physics theoretical and experimental studies (15 papers). S. Vallecorsa is often cited by papers focused on Quantum Computing Algorithms and Architecture (30 papers), Quantum Information and Cryptography (17 papers) and Particle physics theoretical and experimental studies (15 papers). S. Vallecorsa collaborates with scholars based in Switzerland, Spain and United States. S. Vallecorsa's co-authors include Michele Grossi, Federico Carminati, Elías F. Combarro, Antonio Mandarino, Cenk Tüysüz, José Ranilla, F. Sánchez, Pavel Lougovski, Xi Li and Chen Wu and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and New Journal of Physics.

In The Last Decade

S. Vallecorsa

50 papers receiving 561 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Vallecorsa Switzerland 15 380 148 137 59 53 58 569
Raban Iten Switzerland 7 320 0.8× 20 0.1× 131 1.0× 26 0.4× 80 1.5× 9 507
M. Stipčević Croatia 14 184 0.5× 80 0.5× 183 1.3× 65 1.1× 23 0.4× 42 452
Hongyi Zhou China 13 677 1.8× 130 0.9× 595 4.3× 104 1.8× 28 0.5× 40 910
Liangjian Wen China 15 138 0.4× 391 2.6× 33 0.2× 158 2.7× 10 0.2× 63 697
Gabriel Perdue United States 10 154 0.4× 65 0.4× 92 0.7× 37 0.6× 17 0.3× 30 290
Jean-Roch Vlimant United States 13 272 0.7× 219 1.5× 55 0.4× 44 0.7× 32 0.6× 39 502
Sébastien Racanière United States 11 154 0.4× 120 0.8× 50 0.4× 38 0.6× 20 0.4× 23 427
Hongcheng Ni China 14 68 0.2× 81 0.5× 666 4.9× 20 0.3× 25 0.5× 51 808
E. Valiante United Kingdom 9 202 0.5× 76 0.5× 37 0.3× 12 0.2× 78 1.5× 16 678
Daliang Li United States 14 147 0.4× 397 2.7× 68 0.5× 132 2.2× 6 0.1× 19 696

Countries citing papers authored by S. Vallecorsa

Since Specialization
Citations

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

Fields of papers citing papers by S. Vallecorsa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Vallecorsa

This figure shows the co-authorship network connecting the top 25 collaborators of S. Vallecorsa. A scholar is included among the top collaborators of S. Vallecorsa 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 S. Vallecorsa. S. Vallecorsa 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.
Ceschini, Andrea, et al.. (2025). A study on quantum graph neural networks applied to molecular physics. Physica Scripta. 100(6). 65126–65126. 1 indexed citations
2.
Grossi, Michele, et al.. (2024). Qutrit quantum battery: Comparing different charging protocols. Physical Review Research. 6(2). 21 indexed citations
3.
Grossi, Michele, et al.. (2024). Counterdiabatic optimized driving in quantum phase sensitive models. New Journal of Physics. 26(3). 33031–33031. 9 indexed citations
4.
Rudolph, Manuel S., et al.. (2024). Trainability barriers and opportunities in quantum generative modeling. npj Quantum Information. 10(1). 16 indexed citations
5.
Biassoni, M., A. Giachero, Michele Grossi, et al.. (2024). Assessment of few-hits machine learning classification algorithms for low-energy physics in liquid argon detectors. The European Physical Journal Plus. 139(8). 4 indexed citations
6.
Tüysüz, Cenk, et al.. (2024). Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise. PRX Quantum. 5(3). 9 indexed citations
7.
Schenk, Michael, et al.. (2024). Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines. Quantum Science and Technology. 9(2). 25012–25012. 6 indexed citations
8.
Grossi, Michele, et al.. (2024). Measurements With A Quantum Vision Transformer: A Naive Approach. SHILAP Revista de lepidopterología. 295. 12003–12003.
9.
Vallecorsa, S., et al.. (2024). Hybrid ground-state quantum algorithms based on neural Schrödinger forging. Physical Review Research. 6(2). 4 indexed citations
10.
Grossi, Michele, M. Pierini, S. Vallecorsa, et al.. (2023). Unravelling physics beyond the standard model with classical and quantum anomaly detection. Machine Learning Science and Technology. 4(4). 45031–45031. 24 indexed citations
11.
Rehm, Florian, et al.. (2023). Precise image generation on current noisy quantum computing devices. Quantum Science and Technology. 9(1). 15009–15009. 3 indexed citations
12.
Vallecorsa, S., et al.. (2023). Ensemble Models for Calorimeter Simulations. Journal of Physics Conference Series. 2438(1). 12080–12080. 2 indexed citations
13.
Borras, K., Lena Funcke, Michele Grossi, et al.. (2023). Impact of quantum noise on the training of quantum Generative Adversarial Networks. Journal of Physics Conference Series. 2438(1). 12093–12093. 11 indexed citations
14.
Mandarino, Antonio, et al.. (2023). Quantum phase detection generalization from marginal quantum neural network models. Physical review. B.. 107(8). 32 indexed citations
15.
Wozniak, K. A., M. Pierini, S. Vallecorsa, et al.. (2023). Quantum machine learning in the latent space of high energy physics events. Journal of Physics Conference Series. 2438(1). 12115–12115.
16.
Rehm, Florian, S. Vallecorsa, Michele Grossi, et al.. (2022). Quantum Angle Generator for Image Generation. CERN Document Server (European Organization for Nuclear Research). 425–429. 1 indexed citations
17.
Tüysüz, Cenk, B. Demirköz, D. Dobos, et al.. (2021). Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction. arXiv (Cornell University). 37 indexed citations
18.
Rehm, Florian, S. Vallecorsa, K. Borras, & D. Krücker. (2021). QUANTUM MACHINE LEARNING FOR HEP DETECTOR SIMULATIONS. 363–368. 4 indexed citations
19.
Rehm, Florian, S. Vallecorsa, K. Borras, & D. Krücker. (2021). BENCHMARK OF GENERATIVE ADVERSARIAL NETWORKS FOR FAST HEP CALORIMETER SIMULATIONS. 310–315. 1 indexed citations
20.
Vallecorsa, S., et al.. (2021). Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics. Springer Link (Chiba Institute of Technology). 14 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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