Quantum-Enabled Identification of Microbial Subcommunities Associated with Stability and VFA Selectivity in Anaerobic Fermentation

Mixed-cultured anaerobic fermentation (AF) is increasingly deployed for resource recovery from waste streams, yet process instability and unpredictable volatile fatty acid (VFA) selectivity limit performance. To identify the influential microbes or multimicrobe relationships affecting AF performance, we use a photonic quantum computing algorithm to identify microbial cliques (e.g., strict subcommunities of co-occurring taxa) from two pilot-scale AF reactors, which differed in the organic loading rate (OLR), stability, and VFA profiles. A greater yield of VFA was recovered from the low-OLR reactor compared to the high-OLR reactor (74.11 g VFA kg–1 total solids (TS) in 33 days compared to 24.85 g VFA kg–1 TS in 37 days, respectively). Using 16S rRNA sequencing data, we constructed Jaccard similarity graphs for photonic quantum computing analysis, which revealed clear differences in community organization and functional capabilities. The high-OLR reactor was characterized by highly overlapping acidogenic cliques, whereas the low-OLR reactor contained fragmented cliques of acidogenic taxa alongside low-abundant methanogenic (Methanosarcina) and syntrophic (Syntrophomonas) microbes. The potential effect of these metabolisms in AF warrants further experimental investigation. This work introduces a quantum-assisted probabilistic clique prioritization model to improve exploratory microbial network analysis as a tool for designing engineering interventions in AF.