Giacomo Torlai

2.1k total citations · 1 hit paper
16 papers, 1.1k citations indexed

About

Giacomo Torlai is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, Giacomo Torlai has authored 16 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Atomic and Molecular Physics, and Optics, 11 papers in Artificial Intelligence and 3 papers in Statistical and Nonlinear Physics. Recurrent topics in Giacomo Torlai's work include Quantum many-body systems (10 papers), Quantum Computing Algorithms and Architecture (9 papers) and Neural Networks and Applications (3 papers). Giacomo Torlai is often cited by papers focused on Quantum many-body systems (10 papers), Quantum Computing Algorithms and Architecture (9 papers) and Neural Networks and Applications (3 papers). Giacomo Torlai collaborates with scholars based in Canada, United States and Switzerland. Giacomo Torlai's co-authors include Roger G. Melko, Juan Carrasquilla, Leandro Aolita, Hsin-Yuan Huang, Victor V. Albert, John Preskill, Richard Kueng, Evert van Nieuwenburg, Giuseppe Carleo and Alexander Keesling and has published in prestigious journals such as Science, Physical Review Letters and Nature Communications.

In The Last Decade

Giacomo Torlai

16 papers receiving 1.1k citations

Hit Papers

align trajectories sort by overperformance

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.

Provably efficient machine learning for quantum many-body problems

2022 150 citations Hsin-Yuan Huang, Richard Kueng et al. Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Giacomo Torlai Canada	12	721	711	199	161	111	16	1.1k
Dong-Ling Deng China	25	1.3k 1.8×	800 1.1×	325 1.6×	293 1.8×	251 2.3×	68	1.8k
Vinay Ramasesh United States	8	847 1.2×	724 1.0×	78 0.4×	108 0.7×	27 0.2×	12	1.1k
Tobias Haug Singapore	18	1.2k 1.6×	1.2k 1.7×	111 0.6×	184 1.1×	107 1.0×	41	2.0k
Dries Sels United States	19	1.0k 1.4×	535 0.8×	382 1.9×	199 1.2×	56 0.5×	61	1.2k
Ken Xuan Wei United States	10	790 1.1×	791 1.1×	143 0.7×	84 0.5×	26 0.2×	12	1.1k
Yu-Ran Zhang China	20	1.5k 2.0×	815 1.1×	370 1.9×	89 0.6×	101 0.9×	46	1.6k
Chu Guo China	18	638 0.9×	587 0.8×	143 0.7×	91 0.6×	36 0.3×	71	947
Toby S. Cubitt United Kingdom	16	1.1k 1.5×	1.1k 1.5×	268 1.3×	79 0.5×	26 0.2×	39	1.4k
David Gosset United States	17	704 1.0×	1.1k 1.5×	71 0.4×	60 0.4×	28 0.3×	39	1.3k
Leonid P. Pryadko United States	19	705 1.0×	477 0.7×	104 0.5×	367 2.3×	47 0.4×	67	1.1k

Countries citing papers authored by Giacomo Torlai

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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16 of 16 papers shown

Torlai, Giacomo, et al.. (2025). Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice. Communications Physics. 8(1). 4 indexed citations

Torlai, Giacomo, et al.. (2023). Quantum process tomography with unsupervised learning and tensor networks. Nature Communications. 14(1). 2858–2858. 40 indexed citations

Huang, Hsin-Yuan, Richard Kueng, Giacomo Torlai, Victor V. Albert, & John Preskill. (2022). Provably efficient machine learning for quantum many-body problems. Science. 377(6613). eabk3333–eabk3333. 150 indexed citations breakdown →

Carrasquilla, Juan & Giacomo Torlai. (2021). How To Use Neural Networks To Investigate Quantum Many-Body Physics. PRX Quantum. 2(4). 47 indexed citations

Torlai, Giacomo, et al.. (2021). Simulating a measurement-induced phase transition for trapped-ion circuits. Physical review. A. 104(6). 26 indexed citations

Carleo, Giuseppe, Kenny Choo, James E. T. Smith, et al.. (2019). NetKet: A machine learning toolkit for many-body quantum systems. Repository for Publications and Research Data (ETH Zurich). 68 indexed citations

Carrasquilla, Juan, Giacomo Torlai, Roger G. Melko, & Leandro Aolita. (2019). Reconstructing quantum states with generative models. Nature Machine Intelligence. 1(3). 155–161. 189 indexed citations

Torlai, Giacomo & Roger G. Melko. (2019). Machine-Learning Quantum States in the NISQ Era. Annual Review of Condensed Matter Physics. 11(1). 325–344. 72 indexed citations

Torlai, Giacomo, Evert van Nieuwenburg, Harry Levine, et al.. (2019). Integrating Neural Networks with a Quantum Simulator for State Reconstruction. Physical Review Letters. 123(23). 230504–230504. 92 indexed citations

10.

Albergo, Michael S., et al.. (2019). Learnability scaling of quantum states: Restricted Boltzmann machines. Physical review. B.. 100(19). 31 indexed citations

11.

Carrasquilla, Juan, Giacomo Torlai, Roger G. Melko, & Leandro Aolita. (2019). Author Correction: Reconstructing quantum states with generative models. Nature Machine Intelligence. 1(4). 200–200. 1 indexed citations

12.

Torlai, Giacomo, et al.. (2018). Schmidt gap in random spin chains. Physical review. B.. 98(8). 4 indexed citations

13.

Torlai, Giacomo & Roger G. Melko. (2018). Latent Space Purification via Neural Density Operators. Physical Review Letters. 120(24). 82 indexed citations

14.

Torlai, Giacomo & Roger G. Melko. (2017). Neural Decoder for Topological Codes. Physical Review Letters. 119(3). 30501–30501. 113 indexed citations

15.

Torlai, Giacomo & Roger G. Melko. (2016). Learning thermodynamics with Boltzmann machines. Physical review. B.. 94(16). 170 indexed citations

16.

Torlai, Giacomo, Petr Marek, Radim Filip, et al.. (2013). Violation of Bell's inequalities with preamplified homodyne detection. Physical Review A. 87(5). 10 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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