Adaptive Quantum Approximate Optimization for Genomic Classification

This paper introduces an Adaptive Quantum Approximate Optimization Algorithm (QAOA) that utilizes gradient-driven operator selection to dynamically construct problem-specific circuits, demonstrating superior precision, specificity, and hardware efficiency over fixed-architecture variants for genomic sequence classification under realistic NISQ noise constraints.

Original authors: Sthefanie Jofer Gomes Passo, Vishal H. Kothavade, John J. Prevost