Classifying Hotspots Mutations for Biosimulation with Quantum Neural Networks and Variational Quantum Eigensolver

TL;DR

This paper presents a quantum computing framework combining Quantum Neural Networks and Variational Quantum Eigensolvers to classify gene mutations and estimate molecular energies, demonstrating high accuracy on current quantum devices.

Contribution

It introduces a novel hybrid quantum approach for mutation classification and energy estimation, advancing the application of quantum computing in computational biology.

Findings

High accuracy in mutation classification on NISQ devices

Effective energy estimation using VQE in biological molecules

Framework serves as a blueprint for future quantum-biology applications

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.