Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice
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2025
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| author | Li, Rui-Hao Doga, Hakan Raubenolt, Bryan Mostame, Sarah DiSanto, Nicholas Cumbo, Fabio Joshi, Jayadev Linn, Hanna Gaffney, Maeve Holden, Alexander Kulkarni, Vinooth Chaudhary, Vipin Merz Jr, Kenneth M. Saki, Abdullah Ash Radivoyevitch, Tomas DiFilippo, Frank Qin, Jun Shehab, Omar Blankenberg, Daniel |
| author_facet | Li, Rui-Hao Doga, Hakan Raubenolt, Bryan Mostame, Sarah DiSanto, Nicholas Cumbo, Fabio Joshi, Jayadev Linn, Hanna Gaffney, Maeve Holden, Alexander Kulkarni, Vinooth Chaudhary, Vipin Merz Jr, Kenneth M. Saki, Abdullah Ash Radivoyevitch, Tomas DiFilippo, Frank Qin, Jun Shehab, Omar Blankenberg, Daniel |
| contents | In this work, we present the first implementation of the face-centered cubic (FCC) lattice model for protein structure prediction with a quantum algorithm. Our motivation to encode the FCC lattice stems from our observation that the FCC lattice is more capable in terms of modeling realistic secondary structures in proteins compared to other lattices, as demonstrated using root mean square deviation (RMSD). We utilize two quantum methods to solve this problem: a polynomial fitting approach (PolyFit) and the Variational Quantum Eigensolver with constraints (VQEC) based on the Lagrangian duality principle. Both methods are successfully deployed on Eagle R3 (ibm_cleveland) and Heron R2 (ibm_kingston) quantum computers, where we are able to recover ground state configurations for the 6-amino acid sequence KLVFFA under noise. A comparative analysis of the outcomes generated by the two QPUs reveals a significant enhancement (reaching nearly a two-fold improvement for PolyFit and a three-fold improvement for VQEC) in the prediction and sampling of the optimal solution (ground state conformations) on the newer Heron R2 architecture, highlighting the impact of quantum hardware advancements for this application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08955 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice Li, Rui-Hao Doga, Hakan Raubenolt, Bryan Mostame, Sarah DiSanto, Nicholas Cumbo, Fabio Joshi, Jayadev Linn, Hanna Gaffney, Maeve Holden, Alexander Kulkarni, Vinooth Chaudhary, Vipin Merz Jr, Kenneth M. Saki, Abdullah Ash Radivoyevitch, Tomas DiFilippo, Frank Qin, Jun Shehab, Omar Blankenberg, Daniel Quantum Physics Biomolecules In this work, we present the first implementation of the face-centered cubic (FCC) lattice model for protein structure prediction with a quantum algorithm. Our motivation to encode the FCC lattice stems from our observation that the FCC lattice is more capable in terms of modeling realistic secondary structures in proteins compared to other lattices, as demonstrated using root mean square deviation (RMSD). We utilize two quantum methods to solve this problem: a polynomial fitting approach (PolyFit) and the Variational Quantum Eigensolver with constraints (VQEC) based on the Lagrangian duality principle. Both methods are successfully deployed on Eagle R3 (ibm_cleveland) and Heron R2 (ibm_kingston) quantum computers, where we are able to recover ground state configurations for the 6-amino acid sequence KLVFFA under noise. A comparative analysis of the outcomes generated by the two QPUs reveals a significant enhancement (reaching nearly a two-fold improvement for PolyFit and a three-fold improvement for VQEC) in the prediction and sampling of the optimal solution (ground state conformations) on the newer Heron R2 architecture, highlighting the impact of quantum hardware advancements for this application. |
| title | Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice |
| topic | Quantum Physics Biomolecules |
| url | https://arxiv.org/abs/2507.08955 |