S. D. Pandey
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Also affiliated: Indian Institute of Soil Science (2021); Indian Council of Agricultural Research (2021–2025); University of Arkansas System (2026); ICAR – National Research Centre on Litchi (2017–2021); Parallel Quantum Solutions (United States) (2026); Indian Institute of Vegetable Research (2015)
Research Areas
Biography and Research Information
OverviewAI-generated summary
S. D. Pandey's research has focused on agricultural science, with publications examining crop varietal characterization, disease resistance, and yield improvement. Work includes the assessment of genetic variability and heritability for yield-contributing traits in rice (Oryza Sativa L.) genotypes and common millet germplasm. Pandey has also investigated integrated pest management strategies for litchi fruit and shoot borers and the effects of organic manures on banana cultivation. More recently, research has extended to include advanced computational topics, such as a quantum mixture of experts framework for scalable quantum neural networks. Pandey has collaborated with Hoang-Quan Nguyen, Xuan-Bac Nguyen, and Ky Luu at the University of Arkansas at Fayetteville on multiple publications.
Metrics
- h-index: 3
- Publications: 14
- Citations: 33
Selected Publications
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Autonomous Agentic Orchestration for Physics-Aware Scientific Discovery: An Integrative Multimodal Framework for 2D Material Characterization (2026)Journal of the Arkansas Academy of Science OpenAlex
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QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling (2026)arXiv (Cornell University) OpenAlex
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QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks (2025)
Collaboration Network
Top Collaborators
- QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
- QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
- QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
- QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
- QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
- QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
- QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
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