Quantum Computing for Biodiversity: Case Study Data, Code, and Results

Overview Technology readiness assessment for quantum computing in biodiversity research by Science Live. Main Finding: Quantum computing methods are technically feasible but do not yet provide practical advantages at current ecological dataset sizes. What We Did PRISMA scoping review found 283 papers at quantum + biodiversity intersection Selected 2 papers for case study implementation Used 1 paper (QOMIC) — the other was a review with no implementable method Applied QOMIC to ecological food web (test dataset NOT from PRISMA) Compared quantum (QAOA) vs classical methods Papers Selected from PRISMA Review Paper DOI Confidence Used QOMIC (Ngo et al. 2024) 10.1093/bioadv/vbae208 0.9 ✅ Yes Quantum Ecology Review 10.48550/arXiv.2504.03866 0.7 ❌ No (review paper) Test Dataset (NOT from PRISMA) Serengeti Food Web - Baskerville et al. 2011 - DOI: 10.1371/journal.pcbi.1002321 Ecological network data (161 species, 592 links) Files File Description serengeti_species.csv 161 species list serengeti_predation.csv 85 mammal predation links run_quantum_bifans.py QAOA quantum simulation run_classical_analysis.py Classical motif analysis quantum_bifan_results.json Quantum results classical_motif_results.json Classical results Results 629 bifans in real network (p=0.023 vs random) Quantum = Classical at 15 qubits (no advantage) Estimated break-even: ~1000+ variables Related Study Assessment: https://w3id.org/np/RAlN5rGFTlXawYWAMdSDMm2SfTh8mfsN9Jhx-Oh7yXR-4 GitHub: https://github.com/ScienceLive/quantum-biodiversity License Code: MIT Data: CC-BY 4.0 Contact Anne Fouilloux - ORCID: 0000-0002-1784-2920