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The Biosis Bulletin: Bioscience and Information science Journal

Quantum Computing Prospects in Bioinformatics

Sofia Ivanov, Ivan Dubois, Isabella Ivanov

Published Oct 8, 2024• 0 citations
Publisher

Abstract

Bioinformatics workloads -- including protein structure prediction, molecular dynamics simulation, genomic sequencealignment, and phylogenetic tree reconstruction -- are among the most computationally intensive problems in science,with many exhibiting exponential or combinatorial scaling that overwhelms classical high-performance computing.Quantum computing, leveraging superposition and entanglement to explore exponentially large solution spaces,promises polynomial or exponential speedups for specific bioinformatics tasks. However, current noisyintermediate-scale quantum (NISQ) devices (50-1,000+ qubits, high error rates) are far from fault-tolerant quantumcomputers, and the practical quantum advantage for bioinformatics remains undemonstrated. This study systematicallybenchmarked five computational approaches -- classical exact algorithms, classical heuristics (genetic algorithm,simulated annealing), GPU-accelerated methods, variational quantum algorithms (VQE, QAOA) on NISQ hardware, anda proposed hybrid quantum-classical bioinformatics pipeline (HQC-Bio) combining quantum subroutines for combinatorialsubproblems with classical pre/post-processing -- across four bioinformatics tasks: multiple sequence alignment (MSA, n= 500 protein families), protein folding on lattice models (HP model, chains 20-60 residues), molecular docking scoring(1,000 ligand-receptor pairs), and phylogenetic tree search (taxa sets 10-50). HQC-Bio achieved the highest solutionquality for protein folding (optimal energy found in 84.6% of 40-residue instances vs 62.4% for simulated annealing; p <0.001) and phylogenetic search (log-likelihood within 0.2% of exact ML for 30-taxa trees vs 1.8% for RAxML heuristic).However, quantum wall-clock time exceeded classical GPU by 12-340x due to NISQ hardware overhead (circuitcompilation, shot noise, error mitigation), establishing that current quantum advantage is in solution quality rather thanspeed. Resource estimation projects that fault-tolerant quantum computers with 10,000-100,000 logical qubits couldachieve genuine speedups of 10-1,000x for these tasks within 10-15 years.

DOI
10.5281/zenodo.19549472

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This paper explores the potential of integrating quantum computing with bioinformatics to revolutionize protein structure prediction. Traditional methods face significant computational challenges, particularly with complex proteins. We propose a novel approach leveraging quantum algorithms to accelerate molecular simulations and integrate biological data for enhanced prediction accuracy. The core claim centers on the ability to achieve more precise protein structure determination through this combined methodology. Specifically, we outline a mechanism utilizing quantum algorithms to drastically reduce simulation times and subsequently, incorporate biological information for optimization. This represents a new paradigm in protein structure prediction, offering a pathway to accurately model protein folding and dynamics. The work delves into the theoretical foundations and potential algorithmic implementations, laying the groundwork for future research and development in this exciting field. The focus is on the computational speedup offered by quantum approaches and the strategic integration with existing bioinformatics techniques.