sub1:assertion {
  <https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ/research-question> dcterms:audience "Biodiversity research domains including species distribution modeling, conservation planning, population genetics, ecological network analysis, and ecosystem dynamics simulation" ;
    dcterms:description "What quantum computing and quantum-inspired approaches have been applied or proposed for biodiversity research and conservation, and what evidence exists for their computational advantages over classical methods?" ;
    dcterms:relation "Classical computational methods including traditional machine learning, simulated annealing, genetic algorithms, and standard statistical approaches (MCMC, maximum likelihood, Bayesian methods)" ;
    dcterms:subject "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" ;
    dcterms:title "Quantum Computing Applications for Biodiversity Research and Conservation: A Scoping Review" ;
    dcterms:type <https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ/descriptive> ;
    schema1:expectedResult "Characterization of application domains, computational advantages demonstrated, hardware requirements, scalability assessments, and readiness for operational biodiversity research and conservation practice" ;
    rdfs:comment "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." .
}