Open Access
| Issue |
EPJ Web Conf.
Volume 360, 2026
1st International Conference on “Quantum Innovations for Computing and Knowledge Systems” (QUICK’26)
|
|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/epjconf/202636001011 | |
| Published online | 23 March 2026 | |
- J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine Learning,” Nature, vol. 549, no. 7671, pp. 195–202, 2017. https://doi.org/10.1038/nature23474 [Google Scholar]
- J. Schuld and N. Killoran, “Is quantum avantage the right goal for quantum machine learning?” PRX Quantum, vol. 3, no. 3, 030101, 2022. https://doi.org/10.1103/PRXQuantum.3.030101 [Google Scholar]
- K. Bharti, A. Cervera-Lierta, T. H. Kyaw, et al., “Noisy intermediate-scale quantum algorithms,” Reviews of Modern Physics, vol. 94, 015004, 2022. https://doi.org/10.1103/RevModPhys.94.015004 [Google Scholar]
- M. Cerezo, A. Sone, T. Volkoff, L. Cincio, and P. J. Coles, “Cost function dependent barren plateaus in shallow quantum neural networks,” Nature Communications, vol. 12, 1791, 2021. https://doi.org/10.1038/s41467-021-21728-w [Google Scholar]
- Y. Du, M.-H. Hsieh, T. Liu, and D. Tao, “Expressive power of parametrized quantum circuits,” Physical Review Research, vol. 2, 033125, 2020. https://doi.org/10.1103/PhysRevResearch.2.033125 [Google Scholar]
- A. Abbas, D. Sutter, C. Zoufal, et al., “The power of quantum neural networks,” Nature Computational Science, vol. 1, pp. 403–409, 2021. https://doi.org/10.1038/s43588-021-00084-1 [Google Scholar]
- H. Huang, R. Kueng, and J. Preskill, “Information-theoretic bounds on quantum advantage in machine learning,” Physical Review Letters, vol. 126, 190505, 2021. https://doi.org/10.1103/PhysRevLett.126.190505 [Google Scholar]
- Q. Ma, C. Hao, N. Si and D. Qu, “Quantum Generative Adversarial Network Enhancement Method Based on Quantum Principal Component Analysis,” 2024 10th International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 1982-1988, 2024. https://ieeexplore.ieee.org/document/10941894 [Google Scholar]
- Y. Wang et al., “Programmable Photonic Nonlinearity for Quantum Simulation,” 2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC), Beijing, China, pp. 1-5, 2024. https://ieeexplore.ieee.org/document/10809484 [Google Scholar]
- M. R. Abiwardani, F. Z. Ruskanda, I. Syafalni, R. Mulyawan and A. Higo, “Generalized Ansatz for High-Accuracy Quantum Sentiment Classification Using Variational Quantum Algorithm Scheme,” in IEEE Access, vol. 13, pp. 157551-157569, 2025. https://ieeexplore.ieee.org/document/11113301 [Google Scholar]
- F. Tacchino et al., “Variational Learning for Quantum Artificial Neural Networks,” in IEEE Transactions on Quantum Engineering, vol. 2, pp. 1-10, 2021, Art no. 3101110. https://ieeexplore.ieee.org/document/9364892 [Google Scholar]
- Cerezo, M., Arrasmith, A., Babbush, R. et al. Variational quantum algorithms. Nat Rev Phys 3, 625–644 (2021). https://doi.org/10.1038/s42254-021-00348-9 [Google Scholar]
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