RinQ: Towards predicting central sites in proteins on current quantum computers
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915489668661248 |
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| author | Mohtashim, Shah Ishmam |
| author_facet | Mohtashim, Shah Ishmam |
| contents | We introduce RinQ, a hybrid quantum-classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave's simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RinQ: Towards predicting central sites in proteins on current quantum computers Mohtashim, Shah Ishmam Quantum Physics Soft Condensed Matter Biological Physics Quantitative Methods We introduce RinQ, a hybrid quantum-classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave's simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach. |
| title | RinQ: Towards predicting central sites in proteins on current quantum computers |
| topic | Quantum Physics Soft Condensed Matter Biological Physics Quantitative Methods |
| url | https://arxiv.org/abs/2508.01501 |