Current - Issue

1. Ramakrishnan, R., et al.: Quantum chemistry structures and properties of 134k molecules. Sci. Data 1, 140022 (2014)
2. Rodríguez-Díaz, F., Gutiérrez-Avilés, D., Troncoso, A., Martínez-Álvarez, F.: A Survey of Quantum Machine Learning: Foundations,
Algorithms, Frameworks, Data and Applications. ACM Computing Surveys 58(4), Article 91, 1–35 (2025). https://doi.org/10.1145/3764582
3. Baldi, P., et al.: Deep learning in drug discovery, bioinformatics, and chemoinformatics. J. Chem. Inf. Model. 54(3), 647–659 (2014)
4. Wu, F., et al.: MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018)
5. Schuld, M., Petruccione, F.: Supervised learning with quantum computers. Springer, Cham (2018)
6. Wittek, P.: Quantum machine learning: what quantum computing means to data mining. Academic Press (2014)
7. Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum algorithms for supervised and unsupervised machine learning. arXiv:1307.0411 (2013)
8. Biamonte, J., et al.: Quantum machine learning. Nature 549, 195–202 (2017)
9. Cao, Y., Romero, J., Olson, J.P., Degroote, M., Johnson, P.D., Kieferová, M., Kivlichan, I.D., Menke, T., Peropadre, B., Sawaya, N.P.D.,
Sim, S., Veis, L., Aspuru-Guzik, A.: Quantum Chemistry in the Age of Quantum Computing. Chemical Reviews 119(19), 10856–10915
(2019). https://doi.org/10.1021/acs.chemrev.8b00803
10. Zhou, Y., Chen, J., Cheng, J., Cao, X., Zhang, Y., Karemore, G., Zitnik, M., Chong, F.T., Liu, J., Fu, T., Liang, Z.: Quantum-machine-
assisted drug discovery. npj Drug Discov. 3, 1 (2026)
11. Schuld, M., Sinayskiy, I., Petruccione, F.: An introduction to quantum machine learning. Contemp. Phys. 56(2), 172–185 (2015)
12. Li, W., Zhang, S.-X., Sheng, Z., Gong, C., Chen, J., Shuai, Z.: Quantum machine learning of molecular energies with hybrid quantum-neural
wavefunction. Digital Discovery 4, 2697–2710 (2025)
13. Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018)
14. McClean, J.R., et al.: The theory of variational hybrid quantum-classical algorithms. New J. Phys. 18, 023023 (2016)
15. Anisurrahman, Alam, B., Hamid, M.: Quantum Machine Learning for Drug Discovery: A Systematic Review. International Journal on Smart
& Sustainable Intelligent Computing 2(2), 80–87 (2025). https://doi.org/10.63503/j.ijssic.2025.15816. Danishuddin, M.A., Haque, M.A., Kumar, V., Khan, S.N., Kim, J.: Quantum intelligence in drug discovery: advancing insights with quantum
machine learning. Drug Discov. Today 30(10), 104463 (2025)
17. Smaldone, A.M., Shee, Y., Kyro, G.W., Xu, C., Vu, N.P., et al.: Quantum Machine Learning in Drug Discovery: Applications in Academia
and Pharmaceutical Industries. Chem. Rev. 125(12), 5436–5460 (2025)
18. Havlíček, V., et al.: Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019)
19. Peruzzo, A., et al.: A variational eigenvalue solver on a photonic quantum processor. Nat. Commun. 5, 4213 (2014)
20. Gómez-Bombarelli, R., et al.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4(2),
268–276 (2018)
21. Kajita, S., Ikeda, K., Imai, M., et al.: Unlocking the potential of quantum machine learning to advance drug discovery. Electronics 12(11),
2402 (2023). https://doi.org/10.3390/electronics12112402
22. Liang, Z., He, Z., Sun, Y., Herman, D., Jiao, Q., Zhu, Y., Jiang, W., Xu, X., Wu, D., Pistoia, M., Shi, Y.: Synergizing quantum techniques
with machine learning for advancing drug discovery challenge. Scientific Reports 14, 31216 (2024)
23. Villalba-Díez, J., et al.: Quantum drug discovery: a hybrid quantum graph neural network–variational quantum eigensolver framework for
serine neutralization. Eur. Phys. J. D 79, 81 (2025)
24. Sree, S.R., Sandhya, M., Jayanthi, A., Pulipati, V., Rao, M.V., Subrahmanyam, D.V.S.S.: Quantum– classical hybrid learning framework
for molecular property prediction and molecule optimization in drug discovery. Discover Computing 29, 358 (2026)
25. Beaulieu, D., Kornjača, M., Krunic, Z., Stivaktakis, M., et al.: Robust Quantum Reservoir Learning for Molecular Property Prediction.
Journal of Chemical Information and Modeling 65(16), 8475–8485 (2025)
26. Shimakawa, H., Kumada, A., Sato, M.: Extrapolative prediction of small-data molecular property using quantum mechanics-assisted
machine learning. npj Computational Materials 10, 11 (2024)
27. Zhang, L., Xu, Y., Wu, M., Wang, L., Xu, H.: Quantum long short-term memory for drug discovery. EPJ Quantum Technology 13, 14
(2026)