# WiMi's Next-Generation Quantum Convolutional Neural Network Reshapes Classical Data Classification Methods

- Link: https://www.thailand-business-news.com/pr-news/wimis-next-generation-quantum-convolutional-neural-network-reshapes-classical-data-classification-methods
- Published: 2026-08-21T21:40:00+07:00
- Author: PR Newswire

BEIJING, Aug. 21, 2026 /PRNewswire/ — WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("
WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology
provider, proposes a cutting-edge quantum machine learning technology oriented toward
classical data classification tasks—a quantum convolutional neural network with 
interaction layers for classical data classification. This technology systematically
enhances the overall performance of quantum convolutional neural networks in terms
of expressive power, entanglement generation capability, and actual classification
performance by introducing a novel interaction layer structure based on three-qubit
interactions, marking an important step forward in the structural design of quantum
deep learning models toward a new phase driven by multi-body interactions.

From the perspective of technical implementation logic, this quantum convolutional
network adopts an overall hybrid quantum-classical architecture design. First, classical
data is mapped to the quantum state space through an efficient data encoding strategy,
ensuring that as much discriminative information from the original data as possible
is preserved under limited qubit resources. For image data, the network employs 
block partitioning and local mapping approaches to embed pixel information into 
corresponding quantum subsystems; for one-dimensional data, a combination of structured
amplitude encoding and angle encoding is used to achieve a compact representation
of data features. After data encoding is completed, the quantum state is fed into
the quantum feature extraction module composed of multiple layers of quantum convolutional
units and interaction layers.

In this module, quantum convolution operations and the novel interaction layers 
are executed alternately. The quantum convolutional layers are responsible for extracting
low-order features within local qubit subspaces, with their structural design adhering
to hardware-friendly principles to avoid introducing excessively deep or difficult-
to-implement quantum gate sequences. The interaction layers serve as the key innovation
of the entire network, achieving cross-channel and cross-scale information fusion
through three-qubit interactions. This design enables the network to significantly
enhance its expressive power for complex patterns while keeping circuit depth under
control. The WiMi R&D team systematically studied the impact of this interaction
layer on the coverage capability of the quantum state space in theoretical analysis.
The results show that after introducing three-body interactions, the set of reachable
states in the parameter space of the network is significantly expanded, effectively
alleviating the common expressivity limitation problem in traditional quantum neural
networks.

In terms of entanglement capability, WiMi further conducted an in-depth analysis
of the proposed network structure from the perspective of quantum information theory.
The study shows that the three-qubit interaction layer can generate high-intensity,
multi-scale entanglement structures at relatively shallow circuit depths, which 
is crucial for quantum machine learning models to capture nonlinear correlations
in the input data. Compared to network structures that rely solely on two-qubit 
entanglement gates, the new model exhibits clear advantages across multiple metrics,
including entanglement entropy, uniformity of entanglement distribution, and efficiency
of entanglement propagation. This characteristic not only enhances the model’s learning
capability but also provides strong support for maintaining stable performance under
the presence of noise.

In terms of the training mechanism, this quantum convolutional neural network employs
a joint iterative approach between classical optimizers and quantum circuit parameters
to complete model learning. The output of the quantum circuit is mapped into classical
feature vectors through measurement, which are then evaluated by a classical loss
function to provide gradient feedback. Addressing the common issues of gradient 
vanishing and optimization instability in quantum model training, WiMi systematically
optimized the parameter initialization strategy and training procedure, enabling
the model to achieve stable convergence in both multi-class and binary classification
tasks. These engineering improvements ensure that the technology possesses advantages
not only at the theoretical level but also has practical deployability and feasibility.

This achievement not only demonstrates the real-world potential of quantum machine
learning in the field of classical data processing, but also provides a replicable
and scalable technical paradigm for the architectural design of next-generation 
quantum intelligent systems. By systematically introducing multi-body quantum interactions
into the structural design of neural networks, WiMi is driving the evolution of 
quantum algorithms from quantum acceleration tools toward quantum-native intelligent
models. This direction is expected to form a synergistic effect with the future 
development of quantum computing hardware, unleashing even more disruptive computational
capabilities.

WiMi plans to further expand the model scale and application scenarios on the basis
of existing technology, including directions such as higher-dimensional image data,
complex time series analysis, and cross-modal data fusion. At the same time, it 
will continue to focus on noise robustness and hardware adaptability issues, promoting
the verification and deployment of this quantum convolutional neural network on 
real quantum devices.

The technical achievement released by WiMi marks a substantial step forward in quantum
machine learning model design, moving from imitating classical structures toward
fully leveraging the intrinsic advantages of quantum physics. By deeply integrating
multi-qubit interaction mechanisms at the network structure level, this technology
provides solid support for performance breakthroughs of quantum convolutional neural
networks in practical applications, and injects new momentum into the industrial
development of quantum artificial intelligence.

**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](http://ir.wimiar.com/).

**Translation Disclaimer**

The original version of this announcement is the officially authorized and only 
legally binding version. If there are any inconsistencies or differences in meaning
between the Chinese translation and the original version, the original version shall
prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make 
no guarantees regarding the translated version and assume no responsibility for 
any direct or indirect losses caused by translation inaccuracies.

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