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Human Genome Set Studied With Quantum Computing

An improved representation of human diversity could lead to personalized medicine and better management of disease outbreaks

Berenice Baker, Editor, Enter Quantum

May 1, 2024

1 Min Read

Researchers are investigating whether quantum computing can speed up the production and analysis of pangenomes, representations of DNA sequences that capture population diversity.

This could offer better insights into an individual’s genome composition that could pave the way for personalized medicine and improved tracking and management of disease outbreaks.

A pangenome is a set of reference human genome sequences that represent the breadth of human genetic diversity better than a single genome.

The study of these -- pangenomics – is one of the most computationally demanding in biomedical science and challenges the capabilities of classical computers.

This new project has been awarded up to $3.5 million under the Wellcome Leap Quantum for Bio-supported Challenge Program that aims to explore the potential of quantum computing for improvements in human health.

It involves researchers based at the University of Cambridge, the Wellcome Sanger Institute and the European Molecular Biology Laboratory European Bioinformatics Institute.

The reference human genome sequence used to gain insights into health, help to diagnose disease or guide medical treatments is based on data from only a few people and does not represent human diversity.

The team aims to develop quantum computing algorithms that could speed the production and analysis of a pangenome reference dataset.  

Related:Aging, Disease Insights Seen Aided by Quantum Computing

Comparing a specific human genome against the human will offer better insights into its unique composition, potentially leading to personalized medicine. Similar approaches for bacterial and viral genomes could help track and manage pathogen outbreaks.

“The structure of many challenging problems in computational genomics, and pangenomics in particular, make them suitable candidates for speedups promised by quantum computing,” said project principal investigator Sergii Strelchuk, from the University of Cambridge.

“We are on a thrilling journey to develop and deploy quantum algorithms tailored to genomic data to gain new insights, which are unattainable using classical algorithms.”

About the Author(s)

Berenice Baker

Editor, Enter Quantum

Berenice is the editor of Enter Quantum, the companion website and exclusive content outlet for The Quantum Computing Summit. Enter Quantum informs quantum computing decision-makers and solutions creators with timely information, business applications and best practice to enable them to adopt the most effective quantum computing solution for their businesses. Berenice has a background in IT and 16 years’ experience as a technology journalist.

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