Quantum Annealing Method Estimates Species Trees
Researchers report QUANTA, a quantum annealing-based approach to species tree estimation that runs on real quantum hardware.
Updated on Nov. 6, 2026 in Quantum Computing
Researchers introduced QUANTA, a quantum annealing-based method for species tree estimation, and ran it on D-Wave quantum annealing hardware for multi-locus species tree inference. The work was posted as a preprint to bioRxiv on November 5, 2026.
Why it matters
Classical bipartitioning heuristics such as Fiduccia-Mattheyses and MaxCut are computationally expensive and limit the scalability of QFM and QMC methods. QUANTA replaces these classical strategies with quantum annealing, offering a route to more scalable phylogenetic analysis.
On the avian phylogenomic dataset of 14,446 genes across 48 genomes, QUANTA reconstructed a species tree with a higher quartet score than wQFM. The bipartition technique achieved a 4100x speedup over FM in wQFM on bipartitions containing 90-200 taxa, though performance on larger taxon sets is not reported.
The players
D-Wave
D-Wave is a quantum computing company that builds quantum annealing hardware, the platform on which QUANTA was run.
The details
QUANTA formulates weighted quartet bipartitioning as a quadratic unconstrained binary optimization (QUBO) problem using the minimum possible number of qubits to represent bipartitions. It was implemented on real D-Wave quantum annealing hardware and is publicly available on GitHub.
Timeline
November 5, 2026: Preprint describing QUANTA posted to bioRxiv.
The Tech Race
QUANTA extends the quartet-based QFM and QMC families by replacing the classical Fiduccia-Mattheyses bipartitioning step with quantum annealing. It is positioned as the first quantum formulation of quartet-based approaches and the first multi-locus species tree method to run on real quantum hardware.
For researchers doing phylogenetic analysis, QUANTA offers an alternative to classical bipartitioning that has been run on real quantum hardware. The source code is publicly available, so labs with access to quantum annealing systems can test the method on their own datasets.
The takeaway
Quantum annealing is now being applied to core phylogenetic inference problems, not just benchmark tasks. Researchers tracking computational biology methods should watch whether QUANTA's accuracy holds as quantum hardware scales.
Further reading
For broader background, explore our Quantum Computing coverage.
More information
The QUANTA publicly available source code repository is available on GitHub.
Source note: This article includes information reported by Biorxiv.
