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

- Link: https://www.thailand-business-news.com/pr-news/wimi-releases-resource-efficient-quantum-convolutional-neural-network-based-on-qram-accelerating-the-practical-implementation-of-large-scale-image-classification-applications
- Published: 2026-07-20T20:00:00+07:00
- Author: PR Newswire

BEIJING, July 20, 2026 /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 operations and massive storage resources. However,
as input image resolution continues to increase and the number of feature channels
keeps growing, the resource demands of CNNs in both training and inference stages
grow exponentially. This not only increases the hardware burden but also limits 
the model’s application space in edge computing, low-power devices, and high-real-
time scenarios. The proposal of quantum convolutional neural networks is precisely
an attempt to significantly compress the required computational resources while 
maintaining model expressiveness through quantum superposition and quantum parallelism.

However, existing QCNN models still face severe challenges at the engineering level.
On one hand, the number of qubits in currently available quantum computing devices
is limited and noise levels are high, making it difficult to directly handle large-
scale input data; on the other hand, many quantum neural network schemes use amplitude
encoding or angle encoding in the data encoding stage, requiring individual loading
of each input sample—this process itself consumes a large amount of circuit depth,
offsetting the potential advantages of quantum computing. How to process large-scale
image data in a one-time, efficient manner under limited quantum resources has become
the core problem constraining the practicalization of QCNNs.

In response to the above issues, WiMi re-examined the data access problem in quantum
neural networks from the architectural level and introduced quantum random access
memory (QRAM) as a key technology into the overall design of quantum convolutional
neural networks. The core idea of QRAM is to use quantum superposition states to
access multiple memory addresses simultaneously, enabling massive classical data
to be indexed and invoked using a logarithmic number of qubits. This characteristic
makes it naturally suitable for combination with the large-scale feature representation
needs in deep learning.

In this released technical scheme, QRAM is not merely used as a simple data loading
tool but is deeply embedded into the feature extraction and channel mapping process
of QCNN, forming an entirely new model structure. After the input image is mapped
into a data structure suitable for quantum storage during the classical preprocessing
stage, the corresponding quantum state representation is constructed through QRAM.
Unlike traditional pixel-by-pixel or block-by-block loading methods, this scheme
allows multiple spatial locations and feature dimensions to exist simultaneously
in the quantum state, thereby enabling parallel access to large-scale input data
in a single quantum operation.

At the convolution computation level, the model proposed by WiMi redefines the implementation
of quantum convolution kernels. In traditional QCNN, convolution operations are 
often realized through local quantum gate combinations, which significantly increase
circuit depth as input scale grows. This technology, leveraging the parallel addressing
capability provided by QRAM, matches convolution kernel parameters and input features
in the form of quantum states, transforming the convolution process into a series
of controlled quantum operations. This design significantly weakens the coupling
relationship between the depth growth of the convolution layer and input size, allowing
the model to maintain a relatively shallow quantum circuit structure even when processing
high-resolution images or multi-channel features.

In terms of output channel expansion, this technology also embodies the design philosophy
of resource efficiency. In classical CNNs, increasing the number of output channels
often leads to linear or even super-linear growth in computation and storage demands,
while in quantum architectures, blindly expanding channels would quickly exhaust
available qubit resources. The channel mapping mechanism based on QRAM proposed 
by WiMi introduces auxiliary index registers in the quantum state, allowing multiple
output channels to exist in superposition within the same quantum circuit, with 
selective readout during measurement to complete classification decisions. This 
mechanism significantly reduces the direct consumption of quantum resources caused
by channel count growth, enabling the model to adapt to more complex classification
tasks.

In the overall training and inference process, this QCNN model adopts a typical 
hybrid quantum-classical architecture. Parameter updates and loss function evaluation
are completed on the classical computing side, while core feature extraction and
mapping processes are handled by quantum circuits. This design fully considers the
current development stage of quantum hardware, avoiding dependence on large-scale
fault-tolerant quantum computers while maximizing the advantages of quantum computing
in high-dimensional feature space processing. Through repeated iterative optimization,
the model achieves a good balance among qubit count, circuit depth, and runtime 
while ensuring classification accuracy.

During the experimental validation phase, WiMi conducted systematic evaluations 
of this model on multiple sets of image classification tasks with different scales.
The results show that, under conditions of significantly increased input data scale
and continuously expanded output channel count, the quantum convolutional neural
network based on QRAM outperforms existing similar QCNN schemes in both resource
consumption and circuit depth while maintaining competitive classification performance.
This result validates the feasibility of this technology in processing large-scale
machine learning tasks in resource-constrained quantum environments.

WiMi’s quantum convolutional neural network for efficient image classification based
on quantum random access memory (QRAM) not only expands the design space of quantum
machine learning models in theory but also provides a practically feasible solution
path for large-scale data processing at the engineering level. With the continuous
improvement of quantum hardware capabilities, the QRAM-based QCNN architecture is
expected to further amplify its parallel computing advantages, offering new solutions
for complex visual tasks and laying a solid foundation for the actual deployment
of future quantum intelligent systems.

**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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