UK-led team loads complete Hepatitis D genome onto a quantum computer in world first
A UK-led team has achieved what it calls a world first by loading a complete genome onto a quantum computer, encoding the Hepatitis D virus’s genetic material as part of an effort to bring quantum computing to the front lines of genomics. The milestone was completed within the Quantum for Bio (Q4Bio) Challenge, an international research programme funded by Wellcome Leap that aims to accelerate quantum applications in human health.
The initiative’s Quantum Pangenomics project is focused on genomic tasks that can strain or exceed classical computers, even when supported by artificial intelligence, including assembling genomes and pangenomes from DNA sequence data and mapping DNA fragments into reference genomes to study variation.
Pangenomes—collections of genome sequences from many individuals of the same species—capture genetic diversity across populations, but they are notoriously difficult to analyse with conventional methods. As more genomes are incorporated, computational complexity grows rapidly, creating bottlenecks for classical tools.
Led by the University of Oxford and the Sanger Institute, the team chose the Hepatitis D virus because its genome is compact and clinically relevant. Hepatitis D can cause a severe, blood-borne liver infection and is spread through contact with infected blood, semen or bodily fluids.
In this context, “loading” the genome means encoding DNA sequence data into a format a quantum computer can process, enabling quantum algorithms to analyse it. The researchers developed efficient quantum circuits to encode bio-sequences in a scheme first proposed more than 25 years ago by University of Melbourne collaborator Professor Lloyd Hollenberg.
They say the successful encoding shows that quantum computers can begin to handle suitably represented, real-world genomic data and indicates an alternative approach to traditional computing in genomics. Next, the team aims to turn the project’s capabilities into a practical tool for the wider scientific community.
Their goal is to package the methods into a service where researchers can upload data, choose a preferred computational route—classical, quantum or a combination—and receive the results. While early, the work represents a step toward quantum-accelerated biological discovery.
Faster, more powerful genomic analysis, the team notes, could help with rapid tracking of infectious disease, deepen understanding of rare genetic disorders and support more precise identification of disease-causing mutations.