Quantum-Enhanced Weighted Gene Co-Expression Network Analysis Reveals Regulatory Networks Underlying Sexual Size Dimorphism in Macrobrachium nipponense.

Weighted gene co-expression network analysis (WGCNA) provides a powerful framework for deciphering the regulatory architecture underlying complex phenotypes in zoological research. However, the strict sample size requirements of traditional WGCNA have limited its applicability to wild or non-model species, where obtaining sufficient biological replicates remains a critical challenge. Here, we developed the quantum-enhanced WGCNA framework by integrating quantum amplitude amplification with traditional topological overlap measures to reconstruct robust networks from limited transcriptomic datasets. The quantum-enhanced framework improved biological signal capture by 2.9%, while preserving 98% topological concordance with traditional networks. Most notably, under 10% noise perturbation, the quantum-enhanced method retained 95.8% of hub genes whereas traditional WGCNA collapsed to 17.9%. Using quantum-enhanced WGCNA, we identified 14 dual biomarkers underlying sexual size dimorphism governing both sex differentiation and growth regulation in Macrobrachium nipponense, forming a densely interconnected regulatory circuit with LOC135207471 and LOC135226538 as central coordinators. This quantum-enhanced WGCNA offers a noise-tolerant analytical strategy for comparative transcriptomics, with broad applicability to conservation genomics of endangered species and evolutionary studies of non-model taxa.