Giacomo Torlai
- Atomic and Molecular Physics, and Optics top 5%
- Artificial Intelligence top 2%
- Statistical and Nonlinear Physics top 5%
- Condensed Matter Physics top 10%
- Materials Chemistry
- Co-authors
- Roger G. MelkoJuan CarrasquillaLeandro AolitaVictor V. AlbertHsin-Yuan HuangRichard KuengJohn PreskillEvert van Nieuwenburg
- Topics
- Quantum many-body systems (10 papers)Quantum Computing Algorithms and Architecture (9 papers)Neural Networks and Applications (3 papers)
- Cited by
- Atomic and Molecular Physics, and OpticsArtificial IntelligenceStatistical and Nonlinear Physics
- Partner nations
- CanadaUnited StatesSwitzerland
In The Last Decade
Giacomo Torlai
16 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 57
- Atomic and Molecular Physics, and Optics 721
- Artificial Intelligence 711
- Statistical and Nonlinear Physics 199
- Condensed Matter Physics 161
- Materials Chemistry 111
Countries citing papers authored by Giacomo Torlai
This map shows the geographic impact of Giacomo Torlai'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 Torlai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giacomo Torlai more than expected).
Fields of papers citing papers by Giacomo Torlai
This network shows the impact of papers produced by Giacomo Torlai. 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 Torlai. The network helps show where Giacomo Torlai may publish in the future.
Co-authorship network of co-authors of Giacomo Torlai
This figure shows the co-authorship network connecting the top 25 collaborators of Giacomo Torlai. A scholar is included among the top collaborators of Giacomo Torlai 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 Torlai. Giacomo Torlai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 40 | |
| 3 | Provably efficient machine learning for quantum many-body problemsbreakdown → | 150 |
| 4 | 47 | |
| 5 | 26 | |
| 6 | 92 | |
| 7 | 68 | |
| 8 | 72 | |
| 9 | 189 | |
| 10 | 31 | |
| 11 | 1 | |
| 12 | 4 | |
| 13 | 82 | |
| 14 | 113 | |
| 15 | 170 | |
| 16 | 10 |
About Giacomo Torlai
Giacomo Torlai is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 16 papers that have together received 1.1k indexed citations. Recurring topics across this work include Quantum many-body systems (10 papers), Quantum Computing Algorithms and Architecture (9 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Atomic and Molecular Physics, and Optics (721 citations), Artificial Intelligence (711 citations) and Statistical and Nonlinear Physics (199 citations). Giacomo Torlai has collaborated with scholars based in Canada, United States and Switzerland. Frequent co-authors include Roger G. Melko, Juan Carrasquilla, Leandro Aolita, Victor V. Albert, Hsin-Yuan Huang, Richard Kueng, John Preskill, Evert van Nieuwenburg, Giuseppe Carleo and Manuel Endres. Their work appears in journals such as Science, Physical Review Letters and Nature Communications.
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.