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WiMi has released an independently developed quantum convolutional neural network (QCNN) designed to overcome current hardware bottlenecks like qubit count and circuit depth for large-scale image classification. By leveraging QRAM, this architecture aims to compress computational resources while maintaining model expressiveness through superposition and parallelism, offering a potential pathway toward practical machine learning on near-term quantum devices.

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WiMi Releases Resource - Efficient Quantum Convolutional Neural Network Based on QRAM , Accelerating the Practical Implementation of Large - Scale Image Classification Applications

GDELTJuly 20, 20266 min read

, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the release of its latest independently developed achievement-a quantum convolutional neural network for efficient image classification based on quantum random access memory (QRAM). This technology is oriented toward typical image classification tasks with large-scale input data and multiple output channels. It systematically addresses the key bottlenecks of existing quantum convolutional neural networks in terms of the number of qubits, circuit depth, and data loading efficiency, providing a feasible path for the practical application of quantum computing in real machine learning scenarios. The success of traditional convolutional neural networks on classical computers relies on highly parallel matrix…

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