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Original Article
Quantum-Enhanced Optimization: A Hybrid Quantum–Classical Framework for Machine Learning
Ayush Gupta1
1 Department of Computer Engineering, Vidyalankar Institute of Technology (University of Mumbai), Maharashtra, India.
Published Online: November-December 2025
Pages: 13-15
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506002
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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