Scientists Load First Complete Genome onto a Quantum Computer in World First, Opening a Path to Quantum Genomics
Researchers from the Wellcome Sanger Institute and University of Oxford encoded the Hepatitis D virus genome onto IBM's 156-qubit Heron processor, marking the first time a complete genome has been processed on quantum hardware.
Overview
Researchers from the Wellcome Sanger Institute and the University of Oxford have accomplished a milestone in quantum biology: for the first time, a complete genome has been encoded and processed on a quantum computer. The team loaded the Hepatitis D virus genome onto an IBM Quantum Heron processor in April 2026, demonstrating that real-world genomic data can now be translated into a form quantum hardware can handle. The result, announced as part of the Wellcome Leap Quantum for Bio (Q4Bio) Challenge, points toward a future in which quantum computing accelerates disease tracking, mutation analysis, and drug discovery at a scale beyond classical methods.
What We Know
The genome chosen for the demonstration was the Hepatitis D virus, a severe blood-borne pathogen whose RNA strand — approximately 1,700 bases long — is the smallest known human viral genome. That compactness made it an ideal first target: according to the Wellcome Sanger Institute, the 1,700 RNA bases fit into just 117 qubits, well within the capacity of IBM’s 156-qubit Heron r2 processor used for the experiment.
The encoding technique draws on theoretical work developed roughly 25 years ago by Professor Lloyd Hollenberg at the University of Melbourne. As the Sanger Institute describes, the team built efficient quantum circuits that translate DNA sequence data into a format quantum processors can manipulate, using a hybrid classical-quantum approach where classical systems handle problem formulation and post-processing while the quantum hardware tackles computationally intensive subproblems.
Dr. Sergii Strelchuk, Associate Professor at the University of Oxford who led the Quantum Pangenomics team, described the challenge in biological terms: “We are building quantum algorithms to help find the best path through this maze when regular tools just get hopelessly stuck,” according to the Sanger Institute. The “maze” in question refers to pangenome graph traversal — the problem of finding relationships between multiple genomes that classical heuristics struggle with at scale.
The work was produced as part of the Q4Bio Challenge, a competitive international research programme funded by Wellcome Leap with a total of $40 million distributed across competing teams. According to IBM’s account of the challenge results, five of the six Phase III finalists chose IBM quantum hardware, with IBM’s Heron r2 noted as “the only available hardware that could meet the Wellcome Leap criteria” of demonstrating algorithms exceeding 50 qubits with circuit depths of 1,000 to 10,000 gates.
The Oxford-Sanger team was one of six finalists, not the prize winner. According to IBM’s announcement, the $2 million Q4Bio prize went to a collaboration between Algorithmiq, Cleveland Clinic, and IBM for simulating photodynamic therapy — a light-activated cancer treatment — on quantum hardware using up to 100 qubits. Sabrina Maniscalco, Algorithmiq’s CEO, stated that the results show “quantum computing can begin to impact real, chemically relevant problems, rather than simplified benchmarks.”
Dr. James McCafferty, Chief Information Officer at the Wellcome Sanger Institute, called the genome encoding “at least one order of magnitude improvement over any other efforts to represent DNA on quantum machines,” according to IBM’s announcement. Sergii Strelchuk added that the result proves “quantum data encoding for genomics is no longer aspirational.”
What We Don’t Know
The demonstration encodes a notably small genome — Hepatitis D’s 1,700 RNA bases sit at the lower bound of what qualifies as a complete genome. Human genomes run to approximately 3.2 billion base pairs. Scaling the approach by six orders of magnitude presents hardware and algorithmic challenges that remain entirely unresolved, and no timeline for reaching clinically relevant genome sizes has been published.
It is also unclear what computational advantage, if any, the quantum approach currently offers over classical methods for the specific tasks demonstrated. The hybrid approach — using quantum hardware only for subproblems, with classical pre- and post-processing — means the quantum component’s performance relative to a purely classical pipeline has not been independently benchmarked.
The research was conducted under a competitive challenge format with a specific funding body, which introduces selection and publication incentives. Independent replication of the genome-encoding results has not yet been reported.
Finally, the team’s stated next step — developing a publicly accessible service enabling researchers to choose between classical, quantum, or hybrid approaches — carries no announced release date, leaving the path from proof-of-concept to usable research infrastructure uncertain.