WiMi Implements a Quantum Kernel Convolution (QKC) Scheme Capable of Running on Current Noisy Intermediate-Scale Quantum (NISQ) Devices

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BEIJING, June 15, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announces the release of a core technology for hybrid Quantum Convolutional Neural Network (QCNN), proposing and implementing a Quantum Kernel Convolution (QKC) scheme capable of running on current noisy intermediate-scale quantum (NISQ) devices, thereby providing a practically feasible engineering path for quantum-enhanced image classification models. The core objective of this technology is not simply to embed quantum circuits into classical neural networks, but rather, starting from the computationally intensive core operation of convolution, to rethink the computational approach to feature extraction and dimensionality reduction. WiMi points out that classical convolutional layers essentially rely on sliding windows and linear weighted summation to accomplish local feature extraction, whereas quantum computing inherently possesses the capability of high-dimensional Hilbert space representation and quantum parallelism. If local image patches can be mapped into quantum states and feature mixing can be achieved through controlled entanglement evolution, it becomes possible to realize an equivalent or even more expressive feature extraction mechanism under a lower parameter scale.WiMi points out that this pooling approach is essentially an information reallocation and selection mechanism, which can achieve dimensionality compression without explicitly discarding information, thereby significantly reducing the computational burden on subsequent quantum circuits and classical networks.At the overall system architecture level, this hybrid QCNN adopts a layered design of classical-quantum synergy. The classical neural network is responsible for completing preliminary normalization of input data, dimensionality adjustment, and final classification decisions, while the quantum convolutional layer is embedded at the critical position of feature extraction, functioning as a quantum acceleration module. This design enables the model to fully leverage mature classical deep learning toolchains while introducing quantum advantages at key computational nodes, thereby avoiding, from an engineering perspective, the scalability issues that fully quantum models face under current hardware conditio...

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