Adaptive Quantum Approximate Optimization for Genomic Classification

Quantum machine learning for genomic sequence classification faces three compounding constraints on current hardware: barren plateaus in variational training, exponential concentration of quantum kernel expressivity in highdimensional feature spaces, and noise accumulation that limits practical circuit depth to fewer than 50 layers on current NISQ devices. Existing quantum genomics studies address these constraints using fixed-architecture circuits without noise-calibrated validation on standardized benchmarks, leaving the potential of adaptive circuit topology largely unexplored. Three QAOA variants are developed and systematically compared to address this gap. Classical QAOA establishes a baseline using fixed p-layer circuits with standard phase and mixing operators. Alternating QAOA introduces structured CNOT entanglement between alternating qubit pairs to evaluate whether non-local correlations improve genomic pattern recognition. The primary contribution, Adaptive QAOA, employs gradient-driven operator selection from feature-weighted Pauli operator pools, generating variable-depth problemspecific circuits that adapt their topology to dataset structure rather than applying universal ans¨atze. All three architectures are evaluated through quantum simulation on two standardized genomic benchmarks: Human Nontata Promoters (36,131 sequences) and Demo Coding vs. Intergenic Sequences (100,000 sequences). Experiments are conducted under both noiseless conditions and noise models calibrated to IBM superconducting hardware specifications, across feature space dimensionalities ranging from 2 to 10 dimensions using 10-fold cross-validation. Adaptive QAOA consistently achieves the highest precision (approximately 75% under noiseless simulation) and maintains perfect specificity across both datasets and noise conditions, results not observed for fixed-depth variants. These performance gains are accompanied by reduced hardware resource consumption, with Adaptive QAOA requiring the fewest qubits and lowest quantum usage time (∼0.52–0.60 s) across most configurations. Superior coherence characteristics — average T1 of 82.12 μs and T2 of 55.45 μs, exceeding Classical QAOA by 13.6% and 113.3% respectively — provide a physical basis for reduced error accumulation under realistic noise constraints. All QAOA variants maintain stable classification performance across all tested dimensionalities, contrasting with compounding errors documented for fixed-architecture quantum kernel methods on high-dimensional genomic data. These results establish that gradient-driven adaptive circuit topology with domain-specific feature weighting represents a more effective design strategy than uniform depth scaling or entanglement-based ansatz modification for near-term quantum genomic classification.