LLM-based PDF screening of 283 papers from title/abstract screening resulted in 2 papers marked as INCLUDE. Both papers selected for detailed full-text analysis with systematic quote extraction and interpretation generation. Selected papers: (1) QOMIC: quantum optimization for motif identification - DOI: https://doi.org/10.1093/bioadv/vbae208, Study Nanopub: https://w3id.org/np/RAvZd0nKPT, Published: 2024 - Selected for replication study and technology readiness assessment (https://doi.org/10.5281/zenodo.18157621, Replication Study Nanopub: https://w3id.org/np/RAWsF2T9Rp9iKz78AQgad-SZjl4MXtocBRcZWW2cNl2Dw); (2) Addressing ecological challenges from a quantum computing perspective - DOI: https://doi.org/10.48550/arXiv.2504.03866, Study Nanopub: https://w3id.org/np/RA382F5M0J, Published: 2025. These papers provided evidence across multiple PICO evaluation framework dimensions including computational advantages demonstrated (19/20 comparisons showing quantum advantage), hardware requirements (qubit limitations, NISQ devices), application domains (statistical tools, network analysis, dynamical systems), and readiness for operational research (state preparation bottlenecks, practical workarounds). From these 2 papers, 7 detailed quotes were extracted with interpretations documenting specific evidence for evaluation framework dimensions.

Anne Fouilloux

2026-02-17T20:25:54.251Z

283 studies identified as potentially relevant to quantum computing applications in biodiversity research. 238 studies with DOIs published as individual Study Inclusion nanopublications. Studies span quantum algorithms, quantum machine learning, ecological modeling, species identification, and conservation optimization.

Anne Fouilloux

2025-12-30T09:00:11.919Z

test...

Tobias Kuhn

2025-11-24T12:20:23.699Z

includes the study results

Tobias Kuhn

2025-11-24T11:57:48.896Z

Tobias Kuhn

2025-11-24T11:57:48.896Z