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Yangyang Li1, Ruijiao Liu1, Xiaobin Hao1
1Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi'an 710071, China; International Research Center for Intelligent Perception and Computation, Xi'an 710071, China; Joint International Research Center for brain-like perception and cognition, Xi'an 710071, China; Collaborative Innovation Center of Quantum Information of Shaanxi Province, Xi'an 710071, China; School of Artificial Intelligence, Xidian University, Xi'an 710071, China.
本研究介绍了EQNAS,这是一种用于量子神经网络 (QNN) 的增强神经架构搜索方法. EQNAS提高了QNN分类的准确性,并减少了参数,解决了当前量子模型的局限性.
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