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- Vol. 5 Issue 6
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Quantum-Enhanced Optimization: A Hybrid Quantum–Classical Framework for Machine Learning
Authors
1 Department of Computer Engineering, Vidyalankar Institute of Technology (University of Mumbai), Maharashtra, India.
Article Information
- Published In
- Volume 5, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 13-15
- Article Type
- Original Article
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References
1. Cerezo, M., et al. "Variational Quantum Algorithms." Nature Reviews Physics, 2021.
2. Schuld, M., Bocharov, A., et al. “Circuit-centric quantum classifiers.” Physical Review A.
3. McClean, J. R., et al. "Barren plateaus in quantum neural network training landscapes." Nature Communications.
4. Biamonte, J., et al. “Quantum Machine Learning.” Nature, 2017.
5. Farhi, E., Goldstone, J., and Gutmann, S. “A Quantum Approximate Optimization Algorithm.” MIT, 2014.
6. Havlíček, V., et al. "Supervised learning with quantum-enhanced feature spaces." Nature, 2019.
7. Schuld, M., et al. Machine Learning with Quantum Computers. Springer.
8. Nielsen, M. & Chuang, I., Quantum Computation and Quantum Information.
2. Schuld, M., Bocharov, A., et al. “Circuit-centric quantum classifiers.” Physical Review A.
3. McClean, J. R., et al. "Barren plateaus in quantum neural network training landscapes." Nature Communications.
4. Biamonte, J., et al. “Quantum Machine Learning.” Nature, 2017.
5. Farhi, E., Goldstone, J., and Gutmann, S. “A Quantum Approximate Optimization Algorithm.” MIT, 2014.
6. Havlíček, V., et al. "Supervised learning with quantum-enhanced feature spaces." Nature, 2019.
7. Schuld, M., et al. Machine Learning with Quantum Computers. Springer.
8. Nielsen, M. & Chuang, I., Quantum Computation and Quantum Information.
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