Hybrid Quantum–Classical Benchmarks for Synthetic DNA Risk Classification: A Fully Simulated Study
DOI:
https://doi.org/10.22105/kmisj.v2i4.98Keywords:
Quantum machine learning, Hybrid quantum-classical models, DNA sequence classification, Genomic risk predicition, Bioinformatics, Deep learning vAbstract
Hybrid quantum–classical architectures offer a promising direction for sequence modeling, yet their empirical behavior on realistic bioinformatics tasks remains insufficiently documented. Here, we present a fully simulated, reproducible benchmark for hybrid Quantum Machine Learning (QML) applied to synthetic Deoxyribonucleic Acid (DNA) disease-risk classification. A dataset of 5,000 sequences (200 bp) across five balanced classes was generated using motif-injection rules with controlled noise, from which 74 biological features were extracted. Baseline models (Convolutional Neural Network (CNN), attention, classical ensemble) were compared against two quantum-hybrid models incorporating a 4- qubit ZZFeatureMap+RealAmplitudes ansatz, executed exclusively on Qiskit Aer Simulator with 1,024 shots. The attention model achieved the highest test accuracy (51.7%), while quantum-hybrid models produced comparable performance (51.1–51.3%), showing no measurable quantum advantage under these settings. The study establishes an honest, fully reproducible baseline for QML in genomics, highlighting current limitations of small-qubit encodings and motivating future work with trainable variational circuits and larger biological datasets.
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