Marcello Benedetti

1.7k total citations
20 papers, 955 citations indexed

About

Marcello Benedetti is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. According to data from OpenAlex, Marcello Benedetti has authored 20 papers receiving a total of 955 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 5 papers in Atomic and Molecular Physics, and Optics and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Marcello Benedetti's work include Quantum Computing Algorithms and Architecture (19 papers), Quantum Information and Cryptography (11 papers) and Neural Networks and Reservoir Computing (4 papers). Marcello Benedetti is often cited by papers focused on Quantum Computing Algorithms and Architecture (19 papers), Quantum Information and Cryptography (11 papers) and Neural Networks and Reservoir Computing (4 papers). Marcello Benedetti collaborates with scholars based in United Kingdom, United States and Colombia. Marcello Benedetti's co-authors include Alejandro Perdomo‐Ortiz, John Realpe-Gómez, Rupak Biswas, Edward Grant, Óscar Perdomo, Yunseong Nam, Vicente Leyton‐Ortega, Yuta Kikuchi, Leonard Wossnig and Simone Severini and has published in prestigious journals such as Scientific Reports, Nature Physics and Science Advances.

In The Last Decade

Marcello Benedetti

18 papers receiving 895 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marcello Benedetti United Kingdom 11 875 291 179 107 40 20 955
Johannes Jakob Meyer Germany 9 885 1.0× 332 1.1× 148 0.8× 88 0.8× 48 1.2× 16 972
Leonard Wossnig United Kingdom 9 1.0k 1.2× 459 1.6× 245 1.4× 93 0.9× 57 1.4× 15 1.2k
Ryan Sweke Germany 11 795 0.9× 326 1.1× 122 0.7× 80 0.7× 37 0.9× 19 879
Kunal Sharma United States 13 1.1k 1.2× 460 1.6× 168 0.9× 109 1.0× 54 1.4× 33 1.2k
Sukin Sim United States 6 917 1.0× 489 1.7× 166 0.9× 91 0.9× 43 1.1× 11 1.0k
Daniel K. Park South Korea 15 685 0.8× 224 0.8× 183 1.0× 105 1.0× 32 0.8× 38 822
Abhinav Anand Canada 6 916 1.0× 474 1.6× 169 0.9× 93 0.9× 47 1.2× 12 1.0k
Joshua Job United States 6 561 0.6× 231 0.8× 127 0.7× 58 0.5× 31 0.8× 10 650
Hermanni Heimonen Singapore 5 888 1.0× 492 1.7× 153 0.9× 84 0.8× 37 0.9× 6 1.0k
Wai‐Keong Mok Singapore 7 949 1.1× 541 1.9× 156 0.9× 103 1.0× 33 0.8× 17 1.1k

Countries citing papers authored by Marcello Benedetti

Since Specialization
Citations

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

Fields of papers citing papers by Marcello Benedetti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcello Benedetti

This figure shows the co-authorship network connecting the top 25 collaborators of Marcello Benedetti. A scholar is included among the top collaborators of Marcello Benedetti 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 Marcello Benedetti. Marcello Benedetti 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.
Self, Chris N., et al.. (2025). Measuring correlation and entanglement between molecular orbitals on a trapped-ion quantum computer. Scientific Reports. 15(1). 28409–28409.
2.
Rosenkranz, Matthias, et al.. (2025). Quantum state preparation for multivariate functions. Quantum. 9. 1703–1703. 2 indexed citations
3.
Benedetti, Marcello, et al.. (2024). Training quantum Boltzmann machines with the β-variational quantum eigensolver. Machine Learning Science and Technology. 5(2). 25017–25017. 5 indexed citations
4.
Benedetti, Marcello, et al.. (2024). On the sample complexity of quantum Boltzmann machine learning. Communications Physics. 7(1). 9 indexed citations
5.
Self, Chris N., et al.. (2024). Protecting expressive circuits with a quantum error detection code. Nature Physics. 20(2). 219–224. 10 indexed citations
6.
Benedetti, Marcello, et al.. (2023). Bayesian learning of parameterised quantum circuits. Machine Learning Science and Technology. 4(2). 25007–25007. 6 indexed citations
7.
Kikuchi, Yuta, et al.. (2023). Realization of quantum signal processing on a noisy quantum computer. npj Quantum Information. 9(1). 27 indexed citations
8.
Kikuchi, Yuta, et al.. (2023). Predicting Gibbs-State Expectation Values with Pure Thermal Shadows. PRX Quantum. 4(1). 20 indexed citations
9.
Kikuchi, Yuta, et al.. (2023). Research data supporting "Realization of quantum signal processing on a noisy quantum computer". Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
10.
Self, Chris N., et al.. (2023). Research data supporting "Protecting Expressive Circuits with a Quantum Error Detection Code". Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
11.
Benedetti, Marcello, et al.. (2021). Variational Inference with a Quantum Computer. Physical Review Applied. 16(4). 28 indexed citations
12.
Grant, Edward, et al.. (2021). Structure optimization for parameterized quantum circuits. Quantum. 5. 391–391. 124 indexed citations
13.
Zhu, Daiwei, Norbert M. Linke, Marcello Benedetti, et al.. (2019). Training of quantum circuits on a hybrid quantum computer. Science Advances. 5(10). eaaw9918–eaaw9918. 127 indexed citations
14.
Benedetti, Marcello, et al.. (2019). A generative modeling approach for benchmarking and training shallow quantum circuits. npj Quantum Information. 5(1). 190 indexed citations
15.
Benedetti, Marcello, Edward Grant, Leonard Wossnig, & Simone Severini. (2019). Adversarial quantum circuit learning for pure state approximation. New Journal of Physics. 21(4). 43023–43023. 50 indexed citations
16.
Perdomo‐Ortiz, Alejandro, Marcello Benedetti, John Realpe-Gómez, & Rupak Biswas. (2018). Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quantum Science and Technology. 3(3). 30502–30502. 143 indexed citations
17.
Benedetti, Marcello, John Realpe-Gómez, & Alejandro Perdomo‐Ortiz. (2018). Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices. Quantum Science and Technology. 3(3). 34007–34007. 65 indexed citations
18.
Realpe-Gómez, John, Marcello Benedetti, Rupak Biswas, & Alejandro Perdomo‐Ortiz. (2017). Quantum-assisted learning of graphical models with arbitrary pairwise connectivity. Bulletin of the American Physical Society. 2017. 8 indexed citations
19.
Benedetti, Marcello, John Realpe-Gómez, Rupak Biswas, & Alejandro Perdomo‐Ortiz. (2016). Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning. Physical review. A. 94(2). 138 indexed citations
20.
Benedetti, Marcello, John Realpe-Gómez, Rupak Biswas, & Alejandro Perdomo‐Ortiz. (2015). Estimation of effective temperatures in quantum annealers for sampling applications: A case study towards deep learning. arXiv (Cornell University). 1 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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