Classifying Hotspots Mutations for Biosimulation with Quantum Neural Networks and Variational Quantum Eigensolver
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
The rapid expansion of biomolecular datasets presents significant challenges
for computational biology. Quantum computing emerges as a promising solution to
address these complexities. This study introduces a novel quantum framework for
analyzing TART-T and TART-C gene data by integrating genomic and structural
information. Leveraging a Quantum Neural Network (QNN), we classify hotspot
mutations, utilizing quantum superposition to uncover intricate relationships
within the data. Additionally, a Variational Quantum Eigensolver (VQE) is
employed to estimate molecular ground-state energies through a hybrid
classical-quantum approach, overcoming the limitations of traditional
computational methods. Implemented using IBM Qiskit, our framework demonstrates
high accuracy in both mutation classification and energy estimation on current
Noisy Intermediate-Scale Quantum (NISQ) devices. These results underscore the
potential of quantum computing to advance the understanding of gene function
and protein structure. Furthermore, this research serves as a foundational
blueprint for extending quantum computational methods to other genes and
biological systems, highlighting their synergy with classical approaches and
paving the way for breakthroughs in drug discovery and personalized medicine.
