Papers
1
Total Citations
2
H-Index
1
About
Zeqian Wang is a researcher whose work lies at the intersection of swarm intelligence, quantum computing, and autonomous robotics. His most notable contribution is the development of a multi-strategy quantum particle swarm optimization (QPSO) algorithm, specifically designed to address the complex challenge of efficient path planning for mobile robots. This innovative approach, detailed in his 2025 paper, integrates quantum-inspired mechanisms with multiple optimization strategies to enhance convergence speed and solution quality in dynamic environments. While his work is still gaining traction, with his key paper already accumulating 2 citations, it represents a significant step forward in applying quantum-inspired metaheuristics to real-world robotic navigation problems. Wang’s research is particularly valuable for students and engineers working on autonomous systems, as it offers a robust framework for tackling NP-hard optimization tasks in robotics. His work signals a promising trajectory in the fusion of quantum computing principles with practical engineering applications.
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Top Papers
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