Quantum annealing versus classical machine learning applied to a simplified computational biology problem
Open accessAbstract
Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.
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QA capability is based on the adiabatic process.
“This unique quantum annealing (QA) capability is based on the so-called adiabatic process 6,7 .”
From Quantum annealing for combinatorial optimization: a benchmarking study (2025) · cites this paper as [6]
Li et al. used quantum annealing to predict binding specificity using a small number of DNA sequences datasets derived from actual binding affinity experiments.
“Li etal. used quantum annealing to predict binding specificity using a small number of DNA sequences datasets derived from actual binding affinity experiments [82].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [82]
Recently proposed quantum algorithms for DNA sequencing and for identifying the most probable binding site of a transcription factor to DNA exist.
“This is the case of the recently proposed quantum algorithm for DNA sequencing [240,241] and for identifying the most probable binding site of a transcription factor to DNA.[242]”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [242]
Quantum approaches have been proposed to improve the classification process, either by development of quantum neural networks or by development of a quantum support.
“As concerns quantum approaches, they have been proposed to improve the classification process [58, 59], either by development of quantum neural networks [60–62] or, more recently, by development of a quantum support”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [59]
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- Authors
- Li, R. Y., Di Felice, R., Rohs, R., & Lidar, D. A.
- Affiliations
- University of Southern California (US)
- Venue
- npj Quantum Information
- Published
- 2018-02-21
- Volume
- 4
- Issue
- 1
- ISSN
- 2056-6387 (ISSN-L)
- PMC
- PMC5891835
- OpenAlex
- W2791137739
- Cited by
- 202
- References
- 53
- Citations by year
- 2026: 11 · 2025: 29 · 2024: 13 · 2023: 30 · 2022: 39 · 2021: 32 · 2020: 26 · 2019: 19 · 2018: 2 · 2014: 1
- FWCI
- 11.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Quantum Information (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (1551064)
- John Simon Guggenheim Memorial Foundation
- California Institute of Technology
- Office of the Director of National Intelligence (W911NF-17-C-0050)
- National Institutes of Health
- Intelligence Advanced Research Projects Activity (W911NF-17-C0050)
- NIH Office of the Director
- National Institute of General Medical Sciences (U01GM103804, R01GM106056)
- Army Research Office (W911NF- 12-1-0523, W911NF-17-C-0050, W911NF)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Biological Computing
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- quantum annealing, quantum machine learning, transcription factor-DNA binding, simulated annealing, simulated quantum annealing, binding affinity ranking, computational biology
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