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Biodiversity research domains including species distribution modeling, conservation planning, population genetics, ecological network analysis, and ecosystem dynamics simulation
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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?
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Classical computational methods including traditional machine learning, simulated annealing, genetic algorithms, and standard statistical approaches (MCMC, maximum likelihood, Bayesian methods)
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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
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Quantum Computing Applications for Biodiversity Research and Conservation: A Scoping Review
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Characterization of application domains, computational advantages demonstrated, hardware requirements, scalability assessments, and readiness for operational biodiversity research and conservation practice
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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.
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Anne Fouilloux
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2025-12-24T17:06:38+00:00
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PICO Research Question: Quantum Computing Applications for Biodiversity Research and Conservation...
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