Open Access
| Issue |
EPJ Web Conf.
Volume 360, 2026
1st International Conference on “Quantum Innovations for Computing and Knowledge Systems” (QUICK’26)
|
|
|---|---|---|
| Article Number | 01029 | |
| Number of page(s) | 17 | |
| DOI | https://doi.org/10.1051/epjconf/202636001029 | |
| Published online | 23 March 2026 | |
- Abbas, A., Sutter, D., Zoufal, C., et al.: The power of quantum neural networks. Nature Computational Science 1(6), 403-409 (2021) [Google Scholar]
- Bergholm, V., Izaac, J., Schuld, M., et al.: Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968 (2018) [Google Scholar]
- Bharti, K., Cervera-Lierta, A., Kyaw, T.H., et al.: Noisy intermediate-scale quantum algorithms. Reviews of Modern Physics 94(1), 015004 (2022) [CrossRef] [Google Scholar]
- Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549(7671), 195-202 (2017) [CrossRef] [PubMed] [Google Scholar]
- Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001) [NASA ADS] [CrossRef] [Google Scholar]
- Brown, G., Pocock, A., Zhao, M.J., Lujan, M.: Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. Journal of Machine Learning Research 13, 27-66 (2012) [Google Scholar]
- Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing 16(5), 1190-1208 (1995) [Google Scholar]
- Caro, M.C., Huang, H.Y., Cerezo, M., et al.: Generalization in quantum machine learning from few training data. Nature Communications 13(1), 4919 (2022) [Google Scholar]
- Cerezo, M., Arrasmith, A., Babbush, R., et al.: Variational quantum algorithms. Nature Reviews Physics 3(9), 625-644 (2021) [Google Scholar]
- Cerezo, M., Sone, A., Volkoff, T., Cincio, L., Coles, P.J.: Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature Communications 12(1), 1791 (2021) [Google Scholar]
- Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Computers & Electrical Engineering 40(1), 16-28 (2014) [Google Scholar]
- Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273-297 (1995) [Google Scholar]
- Dua, D., Graff, C.: UCI machine learning repository (2019), http://archive.ics.uci.edu/ml [Google Scholar]
- Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 (2014) [Google Scholar]
- Farhi, E., Neven, H.: Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002 (2018) [Google Scholar]
- Fontana, E., Cerezo, M., Holmes, Z., et al.: Non-trivial symmetries in quantum landscapes and their resilience to quantum noise. Quantum 6, 804 (2022) [Google Scholar]
- Fujii, K., Nakajima, K.: Harnessing disordered-ensemble quantum dynamics for machine learning. Physical Review Applied 8(2), 024030 (2017) [Google Scholar]
- Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157-1182 (2003) [Google Scholar]
- Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1), 389-422 (2002) [CrossRef] [Google Scholar]
- Hall, M.A., Smith, L.A.: Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper. FLAIRS Conference pp. 235-239 (1999) [Google Scholar]
- Havlicek, V., Cercoles, A.D., Temme, K., et al.: Supervised learning with quantum-enhanced feature spaces. Nature 567(7747), 209-212 (2019) [Google Scholar]
- Huang, H.Y., Broughton, M., Cotler, J., et al.: Quantum advantage in learning from experiments. Science 376(6598), 1182-1186 (2022) [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Huang, H.Y., Broughton, M., Mohseni, M., et al.: Power of data in quantum machine learning. Nature Communications 12(1), 2631 (2021) [Google Scholar]
- Jolliffe, I.T., Cadima, J.: Principal component analysis (2016) [Google Scholar]
- Kandala, A., Mezzacapo, A., Temme, K., et al.: Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 549(7671), 242-246 (2017) [CrossRef] [PubMed] [Google Scholar]
- Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273-324 (1997) [Google Scholar]
- Kubler, J.M., Arrasmith, A., Cincio, L., Coles, P.J.: An adaptive optimizer for measurement-frugal variational algorithms. Quantum 4, 263 (2020) [Google Scholar]
- Larocca, M., Czarnik, P., Sharma, K., et al.: Diagnosing barren plateaus with tools from quantum optimal control. Quantum 6, 824 (2022) [Google Scholar]
- LaRose, R., Coyle, B.: Robust data encodings for quantum classifiers. Physical Review A 102(3), 032420 (2020) [Google Scholar]
- Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., Liu, H.: Feature selection: A data perspective. ACM Computing Surveys 50(6), 1-45 (2017) [Google Scholar]
- Liu, Y., Arunachalam, S., Temme, K.: A rigorous and robust quantum speed-up in supervised machine learning. Nature Physics 17(9), 1013-1017 (2021) [CrossRef] [Google Scholar]
- Louizos, C., Welling, M., Kingma, D.P.: Learning sparse neural networks through L0 regularization. ICLR (2018) [Google Scholar]
- McClean, J.R., Boixo, S., Smelyanskiy, V.N., Babbush, R., Neven, H.: Barren plateaus in quantum neural network training landscapes. Nature Communications 9(1), 4812 (2018) [CrossRef] [PubMed] [Google Scholar]
- Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. Quantum Information & Computation 5(6), 467-473 (2005) [Google Scholar]
- Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226-1238 (2005) [Google Scholar]
- Perez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E., Latorre, J.I.: Data reuploading for a universal quantum classifier. Quantum 4, 226 (2020) [CrossRef] [Google Scholar]
- Peruzzo, A., McClean, J., Shadbolt, P., et al.: A variational eigenvalue solver on a photonic quantum processor. Nature Communications 5(1), 4213 (2014) [CrossRef] [PubMed] [Google Scholar]
- Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018) [CrossRef] [Google Scholar]
- Schuld, M., Bocharov, A., Svore, K.M., Wiebe, N.: Circuit-centric quantum classifiers. Physical Review A 101(3), 032308 (2020) [Google Scholar]
- Schuld, M., Killoran, N.: Quantum machine learning in feature Hilbert spaces. Physical Review Letters 122(4), 040504 (2019) [Google Scholar]
- Schuld, M., Petruccione, F.: Supervised learning with quantum computers. Springer (2018) [Google Scholar]
- Schuld, M., Petruccione, F.: Machine learning with quantum computers. Springer (2021) [Google Scholar]
- Street, W.N., Wolberg, W.H., Mangasarian, O.L.: Nuclear feature extraction for breast tumor diagnosis. Biomedical Image Processing and Biomedical Visualization 1905, 861-870 (1993) [Google Scholar]
- Thanasilp, S., Wang, S., Cerezo, M., Holmes, Z.: Exponential concentration and untrainability in quantum kernel methods. arXiv preprint arXiv:2208.11060 (2022) [Google Scholar]
- Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 58(1), 267-288 (1996) [Google Scholar]
- Wang, S., Fontana, E., Cerezo, M., et al.: Noise-induced barren plateaus in variational quantum algorithms. Nature Communications 12(1), 6961 (2021) [Google Scholar]
- Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B 67(2), 301-320 (2005) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

