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methods)" } ], "http://purl.org/dc/terms/subject": [ { "@value": "Quantum computing approaches and quantum-inspired algorithms, including quantum machine learning, quantum optimization (e.g., quantum annealing, QAOA), quantum-enhanced MCMC, and quantum community detection methods" } ], "http://purl.org/dc/terms/title": [ { "@value": "Quantum Computing Applications for Biodiversity Research and Conservation: A Scoping Review" } ], "http://purl.org/dc/terms/type": [ { "@id": "https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ/descriptive" } ], "http://schema.org/expectedResult": [ { "@value": "Characterization of application domains, computational advantages demonstrated, hardware requirements, scalability assessments, and readiness for operational biodiversity research and conservation practice" } ], "http://www.w3.org/2000/01/rdf-schema#comment": [ { "@value": "Quantum computing is emerging as a potential paradigm shift for ecology (Sherley et al. 2023, Clenet et al. 2025). Applications include conservation planning optimization (Microsoft/Marxan), species distribution modeling, population genetics, and ecological network analysis. This scoping review maps the current landscape to identify promising directions and gaps for the biodiversity research community." } ] } ] } ]