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Motoki Kurokawa is a researcher at the forefront of quantum-inspired computational intelligence, specializing in the design and application of quantum neural networks for advanced control systems. His work bridges the gap between quantum computing principles and practical engineering, with a focus on developing learning-type neural controllers that leverage qubit neurons as their core information processing units. Kurokawa’s most impactful contribution, the 2014 paper “Multi-layer quantum neural network controller trained by real-coded genetic algorithm,” has garnered 36 citations, highlighting its significance in the field. This work introduced a novel approach to optimizing quantum neural network controllers using evolutionary algorithms, offering enhanced performance in complex control tasks. His earlier investigations, including the 2012 study on controller applications of multi-layer quantum neural networks with qubit neurons, further cemented his role in advancing quantum neural control, while his 2011 paper explored the use of conjugate gradient algorithms for training. Together, these studies demonstrate Kurokawa’s commitment to pushing the boundaries of neural network theory and its real-world applications, making him a notable figure in the intersection of quantum computing and control engineering.