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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning for quantum matter
Juan Carrasquilla
Advances in Physics X (2020) Vol. 5, Iss. 1, pp. 1797528-1797528
Open Access | Times Cited: 202

Showing 1-25 of 202 citing articles:

Provably efficient machine learning for quantum many-body problems
Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, et al.
Science (2022) Vol. 377, Iss. 6613
Open Access | Times Cited: 174
Intelligent metaphotonics empowered by machine learning
Sergey Krasikov, Aaron D. Tranter, Andrey Bogdanov, et al.
Opto-Electronic Advances (2022) Vol. 5, Iss. 3, pp. 210147-210147
Open Access | Times Cited: 135
Ab Initio Machine Learning in Chemical Compound Space
Bing Huang, O. Anatole von Lilienfeld
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 10001-10036
Open Access | Times Cited: 126
Artificial intelligence and machine learning for quantum technologies
Mario Krenn, Jonas Landgraf, Thomas Foesel, et al.
Physical review. A/Physical review, A (2023) Vol. 107, Iss. 1
Open Access | Times Cited: 73
Self-powered sensing systems with learning capability
Avinash Alagumalai, Wan Shou, Omid Mahian, et al.
Joule (2022) Vol. 6, Iss. 7, pp. 1475-1500
Open Access | Times Cited: 72
Message-passing neural quantum states for the homogeneous electron gas
Gabriel Pescia, Jannes Nys, Jane Kim, et al.
Physical review. B./Physical review. B (2024) Vol. 110, Iss. 3
Open Access | Times Cited: 19
A Detailed Overview of Quantum Computing Machine Learning Techniques
Ada Aggarwal, S Vikram Singh, Saloni Bansal, et al.
(2024), pp. 1721-1725
Closed Access | Times Cited: 17
Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
Krzysztof M. Graczyk, Maciej Matyka
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 110
Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscape
Marin Bukov, Markus Schmitt, Maxime Dupont
SciPost Physics (2021) Vol. 10, Iss. 6
Open Access | Times Cited: 75
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
Jiahao Yao, Lin Lin, Marin Bukov
Physical Review X (2021) Vol. 11, Iss. 3
Open Access | Times Cited: 67
Classification and reconstruction of optical quantum states with deep neural networks
Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori, et al.
Physical Review Research (2021) Vol. 3, Iss. 3
Open Access | Times Cited: 64
Unsupervised Learning Universal Critical Behavior via the Intrinsic Dimension
Tiago Mendes-Santos, Xhek Turkeshi, Marcello Dalmonte, et al.
Physical Review X (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 57
Measurement-induced criticality as a data-structure transition
Xhek Turkeshi
Physical review. B./Physical review. B (2022) Vol. 106, Iss. 14
Open Access | Times Cited: 49
Neural Error Mitigation of Near-Term Quantum Simulations
Elizabeth R. Bennewitz, Florian Hopfmueller, Bohdan Kulchytskyy, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 7, pp. 618-624
Closed Access | Times Cited: 41
Optimal control of quantum thermal machines using machine learning
Ilia Khait, Juan Carrasquilla, Dvira Segal
Physical Review Research (2022) Vol. 4, Iss. 1
Open Access | Times Cited: 38
Conditional normalizing flow for Markov chain Monte Carlo sampling in the critical region of lattice field theory
Ankur Singha, Dipankar Chakrabarti, Vipul Arora
Physical review. D/Physical review. D. (2023) Vol. 107, Iss. 1
Open Access | Times Cited: 23
Retrieving nonstabilizerness with neural networks
Antonio Francesco Mello, Guglielmo Lami, Mario Collura
Physical review. A/Physical review, A (2025) Vol. 111, Iss. 1
Open Access | Times Cited: 1
Neural-network quantum state tomography in a two-qubit experiment
Marcel Neugebauer, Laurin E. Fischer, Alexander Jäger, et al.
Physical review. A/Physical review, A (2020) Vol. 102, Iss. 4
Open Access | Times Cited: 66
Reducing autocorrelation times in lattice simulations with generative adversarial networks
Jan M. Pawlowski, Julian M. Urban
Machine Learning Science and Technology (2020) Vol. 1, Iss. 4, pp. 045011-045011
Open Access | Times Cited: 62
Quantum machine learning and quantum biomimetics: A perspective
Lucas Lamata
Machine Learning Science and Technology (2020) Vol. 1, Iss. 3, pp. 033002-033002
Open Access | Times Cited: 58
Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, et al.
Physical review. D/Physical review. D. (2021) Vol. 104, Iss. 11
Open Access | Times Cited: 49
Time-Dependent Variational Principle for Open Quantum Systems with Artificial Neural Networks
Moritz Reh, Markus Schmitt, Martin Gärttner
Physical Review Letters (2021) Vol. 127, Iss. 23
Open Access | Times Cited: 49
Investigating topological order using recurrent neural networks
Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla
Physical review. B./Physical review. B (2023) Vol. 108, Iss. 7
Closed Access | Times Cited: 19
Sampling QCD field configurations with gauge-equivariant flow models
Phiala E. Shanahan, Ryan Abbott, Michael S. Albergo, et al.
Proceedings of The 39th International Symposium on Lattice Field Theory — PoS(LATTICE2022) (2023)
Open Access | Times Cited: 16
Advances in artificial intelligence and machine learning for quantum communication applications
Mhlambululi Mafu
IET Quantum Communication (2024) Vol. 5, Iss. 3, pp. 202-231
Open Access | Times Cited: 6
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