Description
In order for quantum computers to reach their full potential in analysing genomic information - quantum bioinformatics [1] - there is a clear need for scalable and general methods for encoding genomic data into quantum states. This work builds on previous research that proposed and implemented a matrix product state based method for quantum state encoding [2] to allow for the encoding of larger data. The use case of genomic workflows, particularly the emergent fields of quantum pangenomics and phylogeny, was highlighted as relying on a robust data encoding scheme targeting current and near-term quantum systems. This study advances on the prior work by implementing tailored depth reduction for circuits encoding genome read data, enabling the full encoding of the genome of the Hepatitis-Delta virus on a quantum computer [3]. These methods are widely applicable to reducing the depth of preparation circuits for discrete data. The application readiness of the encoding is discussed by showing the applicability of quantum sequence alignment [4] on states prepared in the modes discussed. Validation using HPC simulation, and tests on physical quantum computers charters a path to full near-term utility of the method that generalises to the fault-tolerant quantum computing era.
[1] L. C. L. Hollenberg, Fast quantum search algorithms in protein sequence comparisons: Quantum bioinformatics, Physical Review E 62, 7532 (2000), arXiv:quantph/0002076.
[2] F. M. Creevey, H. T. Hassan, J. McCafferty, L. C. L. Hollenberg, and S. Strelchuk, Scalable Quantum State Preparation for Encoding Genomic Data with Matrix Product States, (2025), arXiv:2508.06184.
[2] F. M. Creevey, Hitham T. Hassan, James McCafferty, Lloyd C. L. Hollenberg, Sergii Strelchuk, Encoding a Whole Organism Genome on a Quantum Computer, in prep.
[3] F. M. Creevey, M. Jing, and L. C. L. Hollenberg, Implementation of a quantum sequence alignment algorithm for quantum bioinformatics, (2025), arXiv:2506.22775.
| I am the presenting author | Yes |
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