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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:

Exponential concentration in quantum kernel methods
Supanut Thanasilp, Samson Wang, M. Cerezo, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 18

Showing 18 citing articles:

Quantum machine learning beyond kernel methods
Sofiène Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 111
On fundamental aspects of quantum extreme learning machines
Weijie Xiong, Giorgio Facelli, Mehrad Sahebi, et al.
Quantum Machine Intelligence (2025) Vol. 7, Iss. 1
Open Access | Times Cited: 3
Variational Quantum Simulation: A Case Study for Understanding Warm Starts
Ricard Puig, Marc Drudis, Supanut Thanasilp, et al.
PRX Quantum (2025) Vol. 6, Iss. 1
Open Access | Times Cited: 2
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp, et al.
npj Quantum Information (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 12
A comprehensive review of quantum machine learning: from NISQ to fault tolerance
Yunfei Wang, Junyu Liu
Reports on Progress in Physics (2024) Vol. 87, Iss. 11, pp. 116402-116402
Open Access | Times Cited: 8
Image classification by combining quantum kernel learning and tensor networks
Nathan X. Kodama, Alex Bocharov, Marcus P. da Silva
Physical review. A/Physical review, A (2025) Vol. 111, Iss. 1
Closed Access
Expressivity of deterministic quantum computation with one qubit
Yujin Kim, Daniel K. Park
Physical review. A/Physical review, A (2025) Vol. 111, Iss. 2
Closed Access
Quantum Variational vs. Quantum Kernel Machine Learning Models for Partial Discharge Classification in Dielectric Oils
José Miguel Monzón-Verona, Santiago García–Alonso, Francisco Jorge Santana-Martín
Sensors (2025) Vol. 25, Iss. 4, pp. 1277-1277
Open Access
Estimation of mutual information via quantum kernel methods
Yota Maeda, Hideaki Kawaguchi, Hiroyuki Tezuka
Quantum Machine Intelligence (2025) Vol. 7, Iss. 1
Closed Access
Optimizing quantum convolutional neural network architectures for arbitrary data dimension
Chang Won Lee, Israel F. Araujo, Dong-Ha Kim, et al.
Frontiers in Physics (2025) Vol. 13
Open Access
Data-dependent generalization bounds for parameterized quantum models under noise
Bikram Khanal, Pablo Rivas
The Journal of Supercomputing (2025) Vol. 81, Iss. 4
Closed Access
Barren plateaus in variational quantum computing
Martín Larocca, Supanut Thanasilp, Samson Wang, et al.
Nature Reviews Physics (2025)
Closed Access
Comparative investigation of quantum and classical kernel functions applied in support vector machine algorithms
Ghada Abdulsalam, Irfan Ahmad
Quantum Information Processing (2025) Vol. 24, Iss. 4
Closed Access
Quantum kernel methods under scrutiny: a benchmarking study
Julia A. Schnabel, Marco Roth
Quantum Machine Intelligence (2025) Vol. 7, Iss. 1
Open Access
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
Mehmet Şahin, Benjamin C. B. Symons, Pushpak Pati, et al.
Quantum (2024) Vol. 8, pp. 1502-1502
Open Access | Times Cited: 2
The power of one clean qubit in supervised machine learning
Mahsa Karimi, Ali Javadi-Abhari, Christoph Simon, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 6
Effect of alternating layered Ansätze on trainability of projected quantum kernels
Yudai Suzuki, Muyuan Li
Physical review. A/Physical review, A (2024) Vol. 110, Iss. 1
Closed Access | Times Cited: 1
Coreset selection can accelerate quantum machine learning models with provable generalization
Yiming Huang, Yuan Xiao, Huiyuan Wang, et al.
Physical Review Applied (2024) Vol. 22, Iss. 1
Open Access
Generalization error bound for quantum machine learning in NISQ era—a survey
Bikram Khanal, Pablo Rivas, Arun Sanjel, et al.
Quantum Machine Intelligence (2024) Vol. 6, Iss. 2
Closed Access
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