Open Access Helper

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 26-50 of 202 citing articles:

From Architectures to Applications: A Review of Neural Quantum States
Hannah Lange, Anka Van de Walle, Atiye Abedinnia, et al.
Quantum Science and Technology (2024) Vol. 9, Iss. 4, pp. 040501-040501
Open Access | Times Cited: 6
Machine learning for condensed matter physics
Edwin A. Bedolla-Montiel, Luis Carlos Padierna, Ramón Castañeda-Priego
Journal of Physics Condensed Matter (2020) Vol. 33, Iss. 5, pp. 053001-053001
Open Access | Times Cited: 44
Quantitative and interpretable order parameters for phase transitions from persistent homology
Alex Cole, Gregory J. Loges, Gary Shiu
Physical review. B./Physical review. B (2021) Vol. 104, Iss. 10
Open Access | Times Cited: 39
Solving quasiparticle band spectra of real solids using neural-network quantum states
Nobuyuki Yoshioka, Wataru Mizukami, Franco Nori
Communications Physics (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 39
Interpretable and unsupervised phase classification
Julian Arnold, Frank Schäfer, Martin Žonda, et al.
Physical Review Research (2021) Vol. 3, Iss. 3
Open Access | Times Cited: 35
Intrinsic Dimension of Path Integrals: Data-Mining Quantum Criticality and Emergent Simplicity
Tiago Mendes-Santos, Adriano Angelone, Álex Rodríguez, et al.
PRX Quantum (2021) Vol. 2, Iss. 3
Open Access | Times Cited: 34
Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials
Ryan van Mastrigt, Marjolein Dijkstra, Martin van Hecke, et al.
Physical Review Letters (2022) Vol. 129, Iss. 19
Open Access | Times Cited: 24
A perspective on machine learning and data science for strongly correlated electron problems
Steven Johnston, Ehsan Khatami, Richard Scalettar
Carbon Trends (2022) Vol. 9, pp. 100231-100231
Open Access | Times Cited: 23
Topological data analysis and machine learning
Daniel Leykam, Dimitris G. Angelakis
Advances in Physics X (2023) Vol. 8, Iss. 1
Open Access | Times Cited: 14
Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering
Amin Golzari Oskouei, Nasim Abdolmaleki, Asgarali Bouyer, et al.
Biomedical Signal Processing and Control (2024) Vol. 100, pp. 107063-107063
Closed Access | Times Cited: 5
Hybrid convolutional neural network and projected entangled pair states wave functions for quantum many-particle states
Xiao Liang, Shaojun Dong, Lixin He
Physical review. B./Physical review. B (2021) Vol. 103, Iss. 3
Open Access | Times Cited: 32
U(1)-symmetric recurrent neural networks for quantum state reconstruction
Stewart Morawetz, Isaac J. S. De Vlugt, Juan Carrasquilla, et al.
Physical review. A/Physical review, A (2021) Vol. 104, Iss. 1
Open Access | Times Cited: 32
Revealing the phase diagram of Kitaev materials by machine learning: Cooperation and competition between spin liquids
Ke Liu, Nicolas Sadoune, Nihal Rao, et al.
Physical Review Research (2021) Vol. 3, Iss. 2
Open Access | Times Cited: 27
Quantum kernels to learn the phases of quantum matter
Teresa Sancho-Lorente, Juan Román-Roche, David Zueco
Physical review. A/Physical review, A (2022) Vol. 105, Iss. 4
Open Access | Times Cited: 19
Machine Learning for Optical Scanning Probe Nanoscopy
Xinzhong Chen, Suheng Xu, Sara Shabani, et al.
Advanced Materials (2022) Vol. 35, Iss. 34
Open Access | Times Cited: 19
Extracting electronic many-body correlations from local measurements with artificial neural networks
Faluke Aikebaier, Teemu Ojanen, José L. Lado
SciPost Physics Core (2023) Vol. 6, Iss. 2
Open Access | Times Cited: 12
Topological Kolmogorov complexity and the Berezinskii-Kosterlitz-Thouless mechanism
Vittorio Vitale, Tiago Mendes-Santos, Álex Rodríguez, et al.
Physical review. E (2024) Vol. 109, Iss. 3
Open Access | Times Cited: 4
Steering-induced phase transition in measurement-only quantum circuits
Dongheng Qian, Jing Wang
Physical review. B./Physical review. B (2024) Vol. 109, Iss. 2
Closed Access | Times Cited: 4
Predicting topological invariants and unconventional superconducting pairing from density of states and machine learning
Flávio Noronha, Askery Canabarro, Rafael Chaves, et al.
Physical review. B./Physical review. B (2025) Vol. 111, Iss. 1
Open Access
Universal performance gap of neural quantum states applied to the Hofstadter-Bose-Hubbard model
Eimantas Ledinauskas, Egidijus Anisimovas
SciPost Physics (2025) Vol. 18, Iss. 1
Open Access
Efficient Learning of Long-Range and Equivariant Quantum Systems
Štěpán Šmíd, Roberto Bondesan
Quantum (2025) Vol. 9, pp. 1597-1597
Open Access
Learning thermodynamics and topological order of the two-dimensional XY model with generative real-valued restricted Boltzmann machines
Kai Zhang
Physical review. B./Physical review. B (2025) Vol. 111, Iss. 2
Closed Access
Autoregressive neural quantum states of Fermi Hubbard models
Eduardo Ibarra-García-Padilla, Hannah Lange, Roger G. Melko, et al.
Physical Review Research (2025) Vol. 7, Iss. 1
Open Access
Machine Learning of Two-Electron Reduced Density Matrices for Many-Body Problems
Luis H. Delgado-Granados, LeeAnn M. Sager-Smith, Kristina Trifonova, et al.
The Journal of Physical Chemistry Letters (2025), pp. 2231-2237
Closed Access
Bearing Power Loss Predictions in Wind Turbine Gearbox: An Approach Based on LLMs
Janice Anta Zebaze, Azanzi Jiomekong, Innocent Souopgui, et al.
(2025), pp. 1080-1083
Closed Access

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