#hqnn

Quantum-Classical Hybrid Neural Network

The introduction of WiMi Hologram Cloud Inc.'s Hybrid Quantum-Classical Neural Network (H-QNN) technology advanced quantum machine learning in real life. This new approach, designed for MNIST binary image classification, signals a planned shift from theoretical research to business use. The business needs its intellectual property to weather a rough stock market year, therefore the development is important.

Quantum-Classical Bridge

Image recognition has traditionally used multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs). When analyzing high-dimensional data, these models experience “bottlenecks” such computing expense, gradient vanishing, and overfitting.

At the classical network's front end, the H-QNN has a trainable quantum feature encoding module. Quantum superposition and entanglement allow the system to map raw image input into an increasingly large Hilbert space, expressing properties with much more complexity than conventional approaches. To overcome excessive noise and qubit constraints in current quantum hardware, WiMi devised a “synergistic enhancement” that blends quantum feature mapping with classical deep learning parameter optimization.

Three Essential Elements of Architectural Innovation

Three functional levels of the H-QNN design are strictly separated:

Data Preprocessing: MNIST dataset 28x28 pixel images undergo normalization and binarization. WiMi uses statistical feature distribution-based screening to ensure data is "quantumizable," reducing invalid quantum states.

A parameterized quantum circuit (PQC) is utilized for feature extraction and quantum encoding. The PQC embeds numerical information into quantum amplitudes or phases via rotation (Ry, Rz) and entanglement gates (CNOT, CZ). Thus, every image sample has a unique quantum state space global representation.

Classical Neural Classifier: A lightweight MLP gets quantum stage measurements. The model uses classical backpropagation to adjust quantum circuit characteristics and classical weights. WiMi's hybrid optimization method uses the Parameter Shift Rule to ensure training stability. Using accurate gradient estimation in quantum circuits, this method converges the network.

Success and Scalability of Experiments

After rigorous testing to distinguish handwritten “0” and “1,” the H-QNN showed computational advantages. Experimental results showed that the H-QNN outperformed comparable classical MLP models in classification accuracy. Quantum feature mapping decreases overfitting, since the model enhanced generalization and noise resistance even with less samples.

Computing time decreased, which was remarkable. The H-QNN lowered processing time by 30% compared to traditional deep networks in simulations. As the system developed from 4 to 8 qubits, WiMi reported a nonlinear increase in feature expression capabilities, confirming the quantum feature space's scalability.

Way Forward for Quantum Intelligence

Although the MNIST dataset is used to demonstrate the recent success, WiMi considers the H-QNN a general-purpose framework. The company plans to apply this technology to handwriting recognition, video frame feature extraction, and medical image analysis.

Future study will verify the H-QNN's performance on real quantum hardware, not simulations. WiMi also aims to integrate quantum algorithms like QSVM and quantum convolutional networks into a whole quantum intelligence ecosystem.

See also Pakistan's First National Quantum Computing Hackathon.

Market context and investor prospects WiMi has struggled financially despite technological advances. The company's stock has fallen over 80% in a year and is near its 52-week low. Market watchers say WiMi's 0.11 Price/Book ratio suggests the market is undervaluing the company's technology assets and wide quantum-AI research.