Giacomo De Palma

988 total citations
39 papers, 519 citations indexed

About

Giacomo De Palma is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Statistical and Nonlinear Physics. According to data from OpenAlex, Giacomo De Palma has authored 39 papers receiving a total of 519 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 27 papers in Atomic and Molecular Physics, and Optics and 7 papers in Statistical and Nonlinear Physics. Recurrent topics in Giacomo De Palma's work include Quantum Computing Algorithms and Architecture (26 papers), Quantum Information and Cryptography (26 papers) and Quantum Mechanics and Applications (18 papers). Giacomo De Palma is often cited by papers focused on Quantum Computing Algorithms and Architecture (26 papers), Quantum Information and Cryptography (26 papers) and Quantum Mechanics and Applications (18 papers). Giacomo De Palma collaborates with scholars based in Italy, United States and Denmark. Giacomo De Palma's co-authors include Vittorio Giovannetti, Dario Trevisan, Andrea Mari, Johannes Borregaard, Seth Lloyd, Cambyse Rouzé, Bobak T. Kiani, Daniel Stilck França, Alessio Serafini and Zi-Wen Liu and has published in prestigious journals such as Physical Review Letters, Nature Photonics and IEEE Transactions on Information Theory.

In The Last Decade

Giacomo De Palma

35 papers receiving 495 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Giacomo De Palma Italy 15 394 334 121 32 31 39 519
Gen Kimura Japan 14 341 0.9× 397 1.2× 123 1.0× 33 1.0× 24 0.8× 39 482
Cambyse Rouzé France 11 217 0.6× 186 0.6× 72 0.6× 35 1.1× 39 1.3× 23 322
Nathaniel Johnston Canada 14 958 2.4× 899 2.7× 87 0.7× 59 1.8× 16 0.5× 43 1.0k
Ludovico Lami Germany 17 673 1.7× 634 1.9× 93 0.8× 23 0.7× 10 0.3× 55 781
Michał Studziński Poland 11 351 0.9× 321 1.0× 124 1.0× 19 0.6× 13 0.4× 27 441
Sergii Strelchuk United Kingdom 12 380 1.0× 318 1.0× 33 0.3× 24 0.8× 13 0.4× 31 446
M. E. Shirokov Russia 12 331 0.8× 300 0.9× 91 0.8× 47 1.5× 36 1.2× 65 433
Luc Bouten United States 9 493 1.3× 448 1.3× 85 0.7× 24 0.8× 12 0.4× 16 548
Jukka Kiukas Finland 11 293 0.7× 321 1.0× 97 0.8× 14 0.4× 28 0.9× 35 399
Takayuki Miyadera Japan 15 546 1.4× 590 1.8× 219 1.8× 43 1.3× 12 0.4× 41 677

Countries citing papers authored by Giacomo De Palma

Since Specialization
Citations

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

Fields of papers citing papers by Giacomo De Palma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Giacomo De Palma. 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 Giacomo De Palma. The network helps show where Giacomo De Palma may publish in the future.

Co-authorship network of co-authors of Giacomo De Palma

This figure shows the co-authorship network connecting the top 25 collaborators of Giacomo De Palma. A scholar is included among the top collaborators of Giacomo De Palma 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 Giacomo De Palma. Giacomo De Palma 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.
Palma, Giacomo De, et al.. (2025). Trained Quantum Neural Networks are Gaussian Processes. Communications in Mathematical Physics. 406(4). 1 indexed citations
2.
Palma, Giacomo De, et al.. (2025). Quantitative Convergence of Trained Quantum Neural Networks to a Gaussian Process. Annales Henri Poincaré.
3.
Palma, Giacomo De, et al.. (2025). Entanglement degradation in the presence of Markovian noise: A statistical analysis. Physical review. A. 111(1).
4.
Palma, Giacomo De, et al.. (2024). Classical shadows meet quantum optimal mass transport. Journal of Mathematical Physics. 65(9). 2 indexed citations
5.
Palma, Giacomo De, et al.. (2024). Optical Fibers With Memory Effects and Their Quantum Communication Capacities. IEEE Transactions on Information Theory. 70(12). 8844–8869. 2 indexed citations
6.
Palma, Giacomo De, et al.. (2023). Optimal local work extraction from bipartite quantum systems in the presence of Hamiltonian couplings. Physical review. A. 107(1). 19 indexed citations
7.
Palma, Giacomo De, et al.. (2023). Improving the speed of variational quantum algorithms for quantum error correction. Physical review. A. 108(2). 11 indexed citations
8.
Palma, Giacomo De & Dario Trevisan. (2023). The Wasserstein Distance of Order 1 for Quantum Spin Systems on Infinite Lattices. Annales Henri Poincaré. 24(12). 4237–4282. 3 indexed citations
9.
Palma, Giacomo De, et al.. (2022). Limitations of variational quantum algorithms: a quantum optimal transport approach. arXiv (Cornell University). 61 indexed citations
10.
Palma, Giacomo De & Cambyse Rouzé. (2022). Quantum Concentration Inequalities. Annales Henri Poincaré. 23(9). 3391–3429. 15 indexed citations
11.
Palma, Giacomo De, et al.. (2022). Quantum algorithms for group convolution, cross-correlation, and equivariant transformations. Physical review. A. 106(3). 4 indexed citations
12.
Palma, Giacomo De, Dario Trevisan, Vittorio Giovannetti, & Luigi Ambrosio. (2018). Gaussian optimizers for entropic inequalities in quantum information. Journal of Mathematical Physics. 59(8). 14 indexed citations
13.
Palma, Giacomo De, Bobak T. Kiani, & Seth Lloyd. (2018). Deep neural networks are biased towards simple functions.. arXiv (Cornell University). 3 indexed citations
14.
Rosati, Matteo, Giacomo De Palma, Andrea Mari, & Vittorio Giovannetti. (2017). Optimal quantum state discrimination via nested binary measurements. Physical review. A. 95(4). 13 indexed citations
15.
Palma, Giacomo De, Dario Trevisan, & Vittorio Giovannetti. (2017). Gaussian States Minimize the Output Entropy of One-Mode Quantum Gaussian Channels. Physical Review Letters. 118(16). 160503–160503. 21 indexed citations
16.
Palma, Giacomo De, Dario Trevisan, & Vittorio Giovannetti. (2016). Passive States Optimize the Output of Bosonic Gaussian Quantum Channels. IEEE Transactions on Information Theory. 62(5). 2895–2906. 18 indexed citations
17.
Palma, Giacomo De, Andrea Mari, Seth Lloyd, & Vittorio Giovannetti. (2015). Multimode quantum entropy power inequality. Unicam Scientific Publications (University of Camerino). 18 indexed citations
18.
Palma, Giacomo De, et al.. (2015). Normal form decomposition for Gaussian-to-Gaussian superoperators. Journal of Mathematical Physics. 56(5). 22 indexed citations
19.
Palma, Giacomo De, Alessio Serafini, Vittorio Giovannetti, & M. Cramer. (2015). Necessity of Eigenstate Thermalization. Physical Review Letters. 115(22). 220401–220401. 32 indexed citations
20.
Palma, Giacomo De, Andrea Mari, & Vittorio Giovannetti. (2014). A generalization of the entropy power inequality to bosonic quantum systems. Nature Photonics. 8(12). 958–964. 35 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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