Published on: April 2026
HYBRID QUANTUM-CLASSICAL APPROACH FOR 3D PROTEIN FOLDING USING THE HP MODEL AND QAOA
Maddineni Renuka Chowdary Gajjala Vishnu Vardhan Reddy Ravuri Siva Krishna Reddy Jhogi Ashok Kotte Sai Kumar M. Soumya
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Abstract
Protein folding is a computationally challenging problem because the number of possible structures increases rapidly with sequence length. This paper presents a hybrid quantum-classical framework for protein folding using the Hydrophobic-Polar (HP) model in a three-dimensional lattice.
Valid protein structures are generated using a self-avoiding walk, and their energy is evaluated based on hydrophobic interactions. Simulated annealing is used as the classical opti- mization method, while the Quantum Approximate Optimization Algorithm (QAOA) is used for quantum optimization.
Experimental results show that simulated annealing achieves lower energy values (e.g., -5.0 eV) compared to QAOA (-4.45 eV). Although quantum optimization does not yet outperform classical methods, it produces close approximations and shows potential for future improvements.
How to Cite this Paper
Chowdary, M. R., Reddy, G. V. V., Reddy, R. S. K., Ashok, J., Kumar, K. S. & Soumya, M. (2026). Hybrid Quantum-Classical Approach for 3D Protein Folding using the HP Model and QAOA. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.535
Chowdary, Maddineni, et al.. "Hybrid Quantum-Classical Approach for 3D Protein Folding using the HP Model and QAOA." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.535.
Chowdary, Maddineni,Gajjala Reddy,Ravuri Reddy,Jhogi Ashok,Kotte Kumar, and M. Soumya. "Hybrid Quantum-Classical Approach for 3D Protein Folding using the HP Model and QAOA." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.535.
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References
- A. Dill, “Theory for the folding and stability of globular proteins,”
- Biochemistry, vol. 24, no. 6, pp. 1501–1509, 1985.
- Farhi, J. Goldstone, and S. Gutmann, “A quantum approximate optimization algorithm,” arXiv preprint arXiv:1411.4028, 2014.
- Perdomo-Ortiz, N. Dickson, M. Drew-Brook, G. Rose, and A. Aspuru-Guzik, “Finding low-energy conformations of lattice protein models by quantum annealing,” Scientific Reports, vol. 2, no. 571, 2012.
- Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and
- Teller, “Equation of state calculations by fast computing machines,”
- Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, 1953.
- R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics, vol. 18, no. 2, 2016.
- Havl´ıcˇek, A. D. Co´rcoles, K. Temme, et al., “Supervised learning with quantum-enhanced feature spaces,” Nature, vol. 567, pp. 209–212, 2019.
- Schuld and F. Petruccione, Supervised Learning with Quantum Computers, Springer, 2018.
- Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983.
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