RinQ: Towards predicting central sites in proteins on current quantum computers

Fuente: arXiv
Saved in:
Bibliographic Details
Main Author: Mohtashim, Shah Ishmam
Format: Preprint
Published: 2025
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866915489668661248
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