Quantum Computing Applications in Biomedical Sciences

Quantum computing is emerging as a transformative approach across biomedical sciences. By harnessing phenomena such as superposition and entanglement, quantum processors offer novel pathways to tackle computational challenges beyond the reach of classical machines. In molecular modelling, quantum devices promise enhanced accuracy in simulating complex biomolecular interactions, underpinning advances in drug discovery, protein structure prediction and enzyme design. Optimisation tasks—from genome assembly to personalised treatment scheduling—can leverage quantum algorithms to explore vast solution spaces more efficiently, potentially accelerating genomic analyses and clinical decision-making. Quantum machine learning techniques are also being explored to improve pattern recognition in large biomedical datasets, with applications in medical imaging and diagnostic biomarker identification. Despite current hardware limitations, hybrid quantum-classical methods are demonstrating practical gains in pilot studies, foreshadowing a future in which quantum computing underpins real-time precision healthcare and global biomedical research initiatives.

Research from Nature Portfolio

Recent studies have demonstrated the potential of quantum annealing and quantum-inspired techniques for de novo genome assembly. By mapping overlap-layout-consensus graphs to optimisation Hamiltonians, researchers achieved proof-of-concept assemblies of microbial genomes on quantum annealers, indicating a route to more efficient reconstruction of complex genomic rearrangements. These experiments, conducted on early-generation devices, highlight the promise of quantum-based solutions to overcome computational bottlenecks in high-throughput sequencing and personalised genomic analysis.

Research from all publishers

A comprehensive survey of quantum computing in healthcare has catalogued applications spanning drug discovery, personalised medicine, DNA sequencing and operational optimisation, providing a taxonomy of enabling technologies and open challenges. This work delineates how quantum algorithms could accelerate molecular docking, optimise treatment schedules and enhance medical imaging pipelines, guiding future interdisciplinary efforts.

Advances in protein folding have been reported through resource-efficient variational quantum algorithms. By implementing lattice-based Hamiltonian models on noisy intermediate-scale quantum devices coupled with evolutionary optimisation strategies, researchers achieved successful folding simulations of small peptides, demonstrating tangible speed-ups for energy minimisation in biomolecular systems.

A hybrid classical-quantum workflow has been introduced for computer-aided drug design, integrating classical docking and molecular dynamics with quantum machine learning modules to predict the impact of genetic mutations on ligand binding. Initial case studies on viral proteases indicate that quantum-enhanced models can match or exceed classical baselines, suggesting a viable path towards quantum-accelerated therapeutic development.

Quantum Computing Applications in Biomedical Sciences publication trend

The graph below shows the total number of articles in quantum computing applications in biomedical sciences across all publications each year (not limited to Nature Index journals).

Technical terms

Qubit: The fundamental unit of quantum information, capable of existing in superposition of two states, which enables parallel computation.

Quantum annealing: An optimisation method that exploits quantum tunnelling to find low-energy states of a problem Hamiltonian, often used for combinatorial tasks.

Variational quantum algorithm: A hybrid approach that uses a parameterised quantum circuit and classical feedback loop to approximate solutions to optimisation or simulation problems.

Hamiltonian: An operator describing the total energy of a quantum system, whose ground state often encodes the solution to computational problems.

Hybrid quantum-classical algorithm: A computational scheme that partitions tasks between quantum processors and classical computers to leverage strengths of both platforms.

References

1. The prospects of quantum computing in computational molecular biology. Wiley Interdisciplinary Reviews Computational Molecular Science (2020).
2. Resource-efficient quantum algorithm for protein folding. npj Quantum Information (2021).
3. Quantum Computing for Healthcare: A Review. Future Internet (2023).
4. Genome assembly using quantum and quantum-inspired annealing. Scientific Reports (2021).
5. Insights from incorporating quantum computing into drug design workflows. Bioinformatics (2022).