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- research-question 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." assertion.
- research-question 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" assertion.
- research-question 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?" assertion.
- research-question title "Quantum Computing Applications for Biodiversity Research and Conservation: A Scoping Review" assertion.
- research-question type descriptive assertion.
- research-question audience "Biodiversity research domains including species distribution modeling, conservation planning, population genetics, ecological network analysis, and ecosystem dynamics simulation" assertion.
- research-question relation "Classical computational methods including traditional machine learning, simulated annealing, genetic algorithms, and standard statistical approaches (MCMC, maximum likelihood, Bayesian methods)" assertion.
- research-question expectedResult "Characterization of application domains, computational advantages demonstrated, hardware requirements, scalability assessments, and readiness for operational biodiversity research and conservation practice" assertion.