Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection


  • Enow Takang Achuo Albert Department of Plant Biology, Faculty of Science, University of Yaoundé I, P.O. Box 812, Yaoundé, Center Region, Cameroon
  • Ngalle Hermine Bille Department of Plant Biology, Faculty of Science, University of Yaoundé I, P.O. Box 812, Yaoundé, Center Region, Cameroon
  • Bell Joseph Martin Department of Plant Biology, Faculty of Science, University of Yaoundé I, P.O. Box 812, Yaoundé, Center Region, Cameroon
  • Ngonkeu Mangaptche Eddy Leonard Department of Plant Biology, Faculty of Science, University of Yaoundé I, P.O. Box 812, Yaoundé, Center Region, Cameroon



Plant disease resistance, Marker-assisted plant selection, Genetic markers, Adiabatic quantum computing


The goal of this research was to create a more accurate and efficient method for selecting plants with disease resistance using a combination of genetic markers and advanced machine learning algorithms. A multi-disciplinary approach incorporating genomic data, machine learning algorithms and high-performance computing was employed. First, genetic markers highly associated with disease resistance were identified using next-generation sequencing data and statistical analysis. Then, an adiabatic quantum machine learning algorithm was developed to integrate these markers into a single predictor of disease susceptibility. The results demonstrate that the integrative use of genetic markers and adiabatic quantum machine learning significantly improved the accuracy and efficiency of disease resistance-based marker-assisted plant selection. By leveraging the power of adiabatic quantum computing and genetic markers, more effective and efficient strategies for disease resistance-based marker-assisted plant selection can be developed.


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How to Cite

Albert, E. T. A., Bille, N. H., Martin, B. J., & Leonard, N. M. E. (2023). Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection. Journal of Scientific Agriculture, 7, 63–76.