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• W4223977275 abstract "Artificial neural network has been fully developed in recent years, but as the size of the network grows, the required computing power also grows rapidly. In order to take advantage of the parallel computing of quantum computing to solve the difficulties of large computation in neural network, quantum neural network was proposed. In this paper, based on the pulse coupled neural network (PCNN), quantum pulse coupled neural network (QPCNN) is proposed. In this model, the basic quantum logic gates are utilized to form quantum operation modules, such as quantum full adder, quantum multiplier, and quantum comparator. A quantum image convolution operation applicable to QPCNN is designed employing quantum full adders and neighborhood preparation module. And these modules are employed to complete the operations required for QPCNN. And based on QPCNN, an quantum image segmentation is designed. Meanwhile, the effectiveness of QPCNN is proved by simulation experiments, and the complexity analysis shows that QPCNN has exponential speedup compared with classical PCNN." @default.
• W4223977275 created "2022-04-19" @default.
• W4223977275 creator A5012457469 @default.
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• W4223977275 date "2022-08-01" @default.
• W4223977275 modified "2023-09-24" @default.
• W4223977275 title "Quantum pulse coupled neural network" @default.
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• W4223977275 doi "https://doi.org/10.1016/j.neunet.2022.04.007" @default.
• W4223977275 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35523083" @default.
• W4223977275 hasPublicationYear "2022" @default.
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