Quantum Genomics
Predictive text,for DNA.
The same logic behind your keyboard's next-word suggestion — applied to genomic sequence. This study evaluates two quantum approaches for predicting DNA sequence against classical Markov and Transformer baselines.
Dat Chi (Ryan) Le · Abubakar (Sid) Iliyasu · Gyanateet Dutta
Presented for the International Year of Quantum Science and Technology, with the University of Bradford, Quantinuum, and Bradford 2025 UK City of Culture.
01 — The Idea
From Markov chains to transformers, now to qubits.
Sequence prediction has moved from simple Markov-style transition models to transformer architectures like BERT and GPT. Genomic sequence prediction has real medical stakes — identifying causative mutations, therapeutic targets in tumours, drug response, and disease risk — which makes the case for exploring a quantum approach worth testing directly against those classical methods.
02 — Data & Encoding
256 tokens fit exactly into 8 qubits.
DNA sequences from NCBI are broken into 4-letter tokens (4-mers) using a sliding window, giving a vocabulary of 256 unique tokens — enough to capture local motifs like codons or regulatory sites, and small enough to map onto 8 qubits without wasting hardware.
4-mer
Tokenisation of raw DNA sequence
256
Unique tokens (4 bases ^ 4 positions)
8
Qubits — log₂(256), an exact fit
NCBI
Source dataset for genomic sequences
03 — Two Quantum Approaches
A sampler, and a quantum transformer.
Sampler
QMCMC (QD-HMC)
The same quantum Hamiltonian Monte Carlo sampler used in our financial forecasting work, applied here to Bayesian regression over tokenised genomic sequence — generating calibrated probabilities for the next token via quantum coherent evolution and Metropolis–Hastings acceptance.
Quantum Transformer
Quixer (QNLP)
A quantum analogue to attention, built from a Linear Combination of Unitaries and the Quantum Singular Value Transform (LCU + QSVT). Token embeddings drive a parametrised circuit whose quantum state scales as O(N) with sequence length, against O(N²) for classical attention.
04 — Results
~90% accuracy, ahead of classical baselines.
Both quantum approaches outperformed the classical Markov chain and Transformer baselines, which reached roughly 80%. Quixer with a hybrid classical-prior initialisation (“knowledge injection”) also converged faster than a standard quantum initialisation.
QMCMC also scored 67.0% token accuracy and 0.669 F1; Quixer's figure is approximate, read from validation-accuracy curves.
05 — Impact
Mapped against the UN Sustainable Development Goals.
Good Health & Well-Being
Earlier diagnosis and personalised medicine through better sequence prediction.
Industry, Innovation & Infrastructure
Quantum-classical innovation for next-generation genomic modelling.
Partnerships for the Goals
Interdisciplinary collaboration across universities and industry.
06 — Conclusions & Next Steps
Feasible today; next, real hardware and longer sequences.
Next steps: running on actual quantum hardware (Quantinuum's H-Series), extending to longer 6-mer and 8-mer tokens, chromosome-level sequence, and applications in drug-target interaction, cancer genomics, and metagenomics.