WiMi (NASDAQ: WIMI) announced a hybrid Quantum Convolutional Neural Network (QCNN) using a Quantum Kernel Convolution (QKC) scheme that runs on current NISQ devices. The quantum convolution layer, built with Qiskit, integrates into classical deep learning workflows and is trained end-to-end with a hybrid optimization strategy. Tests on the MNIST dataset indicate comparable accuracy to traditional CNNs with significantly fewer parameters, using an entanglement-based quantum pooling mechanism for dimensionality reduction while preserving key classification information. This announcement details a hybrid QCNN and quantum kernel convolution scheme running on NISQ hardware and validated on the MNIST dataset, emphasizing lower parameter counts with classical-like accuracy. It extends a recent series of quantum AI updates since May 11. Regulatory filings in April highlighted sharply higher 2025 net income of about RMB 347.1 million and stronger liquidity, but also noted China, VIE, and internal-control risks that remain relevant alongside technical progress. Noisy intermediate-scale quantum (NISQ) devices are early-stage quantum computers that can process more information than small laboratory models but are still prone to errors and instability. They are like powerful but imperfect calculators that can tackle certain complex problems faster than regular computers, which makes them important for future advances in technology and finance. Their development could eventually lead to new tools for solving problems currently beyond reach. A quantum convolutional neural network is an advanced computer system that uses principles of quantum physics to analyze complex data more efficiently than traditional methods. It mimics how the brain recognizes patterns but operates at a level that could process vast amounts of information rapidly, potentially uncovering insights that help investors make better decisions. Its development could lead to faster, more accurate predictions in financial markets. A Hilbert space is a mathematical setting like a roomy, infinitely extendable coordinate system where each 'point' represents a possible pattern, signal, or model outcome and distances measure how different those patterns are. For investors, it underpins advanced quantitative tools—used in pricing, risk models and signal processing—by turning complex functions and time series into geometric objects so similarities, projections and optimizations become concrete and computable. Quantum parallelism is a computing property that lets a quantum processor explore many possible answers at once by holding data in overlapping states, instead of testing options one after another. For investors it matters because that simultaneous processing can radically speed up tasks such as drug discovery, complex optimization, or code cracking, potentially reshaping competitiveness, risk and valuation in tech, biotech and cybersecurity—think of scanning an entire shopping cart in a blink rather than scanning items one by one. 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 conditions. In terms of technical implementation, WiMi, based on the Qiskit quantum computing development framework, completed a complete engineering realization from quantum circuit construction, parameterized training, to integration with classical deep learning frameworks. The quantum convolutional layer is encapsulated as a reusable module interface that can be directly embedded into existing deep learning training workflows. During the training process, the model adopts a hybrid optimization strategy: classical backpropagation algorithms are used to update the parameters of the classical network, while the parameter-shift rule is utilized to estimate gradients for the quantum circuit parameters, achieving end-to-end joint training. This implementation path effectively addresses the challenge of gradient propagation between quantum and classical components, providing engineering assurance for the trainability of hybrid models. In the experimental validation phase, WiMi selected the MNIST handwritten digit dataset as the benchmark task and conducted a systematic evaluation of the proposed hybrid QCNN model. The experimental results show that, with a significantly lower number of parameters compared to traditional CNN models, this hybrid model is still able to achieve classification accuracy comparable to that of classical models. Particularly noteworthy is that after replacing some classical convolutional layers with quantum convolutional layers, the overall parameter scale and computational complexity of the model are effectively controlled while maintaining stable convergence performance. These results demonstrate that quantum kernel convolution possesses practical feasibility in real tasks, rather than remaining merely at the theoretical level. Furthermore, through analysis of intermediate quantum states and measurement outcomes, WiMi verified the effectiveness of the entanglement-based quantum pooling mechanism in the dimensionality reduction process. Experiments show that quantum pooling not only compresses feature dimensions but also preserves discriminative information critical to the classification task. This finding provides a new entry point for interpretability research in quantum neural networks and lays the foundation for subsequent extensions to more complex datasets and tasks. This hybrid quantum convolutional neural network technology is not an isolated algorithmic innovation, but rather an important step taken around WiMi's long-term strategic goal of deployable quantum-enhanced artificial intelligence. By emphasizing low depth, modularity, and high compatibility with existing AI ecosystems, this technology provides a realistic path for quantum computing to move from the laboratory to practical applications. In the future, further exploration will be conducted on the application potential of this architecture in higher-resolution images, multi-channel data, and other perception tasks, while continuously optimizing circuit designs in conjunction with the development of quantum hardware. The release of WiMi's hybrid neural network quantum kernel convolution technology marks an important step forward for quantum machine learning, moving from proof-of-concept toward engineering implementation. It not only demonstrates the practical value of quantum computing in real-world image recognition tasks but also provides clear design ideas for the future construction of quantum-classical collaborative computing systems. With the continuous improvement of quantum hardware performance and the ongoing maturation of development toolchains, the hybrid QCNN framework built by WiMi is expected to play a role in a broader range of artificial intelligence applications, becoming an important component of next-generation intelligent computing technology. About WiMi Hologram Cloud WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.

Source: Stock Titan

Read Original Source →