WiMi Studies Hybrid Quantum-Classical Inception Neural Network Model for Image Classification

Rhea-AI Summary

WiMi (NASDAQ: WIMI) proposed a hybrid quantum-classical Inception neural network for image classification that integrates three parallel paths—quantum, classical, and hybrid—to improve performance, efficiency, and robustness. The design uses shallow, high-entanglement quantum circuits, parameterized rotation encoding, and concatenated multi-path features to enhance expressiveness while reducing parameter counts.

The architecture targets better trainability and scalability by replacing deep quantum circuits with parallel shallow circuits and plans future work toward hardware deployment and deeper hybrid structures.

News Market Reaction

+0.56%

1 alert

+0.56% News Effect

On the day this news was published, WIMI gained 0.56%, reflecting a mild positive market reaction.

Data tracked by StockTitan Argus on the day of publication.

Market Reality Check

Price: $1.74 Vol: Pre‑news volume 36,761 vs...

low vol

$1.74 Last Close

Volume Pre‑news volume 36,761 vs 20‑day average 110,351, indicating relatively muted trading interest. low

Technical Price $1.77 is trading below the 200‑day MA of $3.39 and near the 52‑week low of $1.75.

Peers on Argus

While WIMI showed a -2.75% pre‑news decline, peers were mixed: ABLV -1.5%, ACCS ...

While WIMI showed a -2.75% pre‑news decline, peers were mixed: ABLV -1.5%, ACCS -0.72%, SWAG -5.71%, FLNT +0.66%, MCTR +0.65%. No peers appeared in the momentum scanner, suggesting the move was stock‑specific rather than a broad Communication Services rotation.

Previous AI Reports

5 past events · Latest: Feb 06 (Positive)

Same Type Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Feb 06	AI quantum update	Positive	+11.5%	Hybrid quantum-classical neural network for MNIST image classification with faster simulation.
Jan 05	AI quantum update	Positive	+10.9%	Next-generation quantum convolutional neural network for multi-channel supervised learning.
Dec 22	AI quantum update	Positive	-1.4%	Hybrid quantum neural network structure targeting image multi-classification bottlenecks.
Dec 22	AI quantum update	Positive	-1.4%	Repeat disclosure of hybrid quantum neural network multi-classification design details.
Oct 23	AI quantum update	Positive	+5.9%	Shallow hybrid quantum-classical convolutional neural network research for image classification.

Pattern Detected

Recent AI‑tagged quantum/AI announcements often coincided with positive moves, but two events saw small declines, indicating mixed follow‑through despite generally upbeat technical news.

Recent Company History

Over the past few months, WiMi has repeatedly highlighted AI and quantum-augmented architectures. Releases on Oct 23, 2025 (SHQCNN) and Dec 22, 2025 (hybrid H-QNN) focused on shallow, hardware-aware quantum nets. Subsequent AI updates on Jan 5, 2026 and Feb 6, 2026 emphasized multi-channel QCNNs and hybrid quantum-classical networks, with several drawing double-digit single-day gains. Today’s AI-tagged hybrid Inception model continues this quantum-enhanced image-classification trajectory.

Historical Comparison

+5.1% avg move · Over the last five AI-tagged quantum/AI releases, WIMI’s average 24-hour move was 5.12%. This hybrid...

AI

+5.1%

Average Historical Move AI

Over the last five AI-tagged quantum/AI releases, WIMI’s average 24-hour move was 5.12%. This hybrid quantum-classical Inception image-classification study fits the same theme of incremental, research-focused AI advances.

AI-tagged news shows a progression from shallow hybrid CNNs (SHQCNN) to broader hybrid quantum neural networks and multi-channel QCNNs, with the latest work extending into Inception-style hybrid architectures for image classification.

Market Pulse Summary

This announcement highlights WiMi’s continued focus on hybrid quantum-classical AI, introducing an I...

Analysis

This announcement highlights WiMi’s continued focus on hybrid quantum-classical AI, introducing an Inception-style network that blends quantum circuits and classical paths for image classification. Recent AI-tagged updates, averaging 5.12% 24-hour moves, have followed a similar research-heavy theme. Investors may watch for concrete deployment steps, integration with real quantum hardware, and clearer commercial use cases to gauge how this work might influence longer-term fundamentals.

Key Terms

quantum computing, hilbert space, parameterized quantum gates, entanglement structures, +3 more

7 terms

quantum computing technical

"integrates quantum computing with classical deep learning through Inception-style"

Quantum computing is a type of advanced technology that uses the principles of quantum physics to perform calculations much faster than traditional computers. It can process vast amounts of information simultaneously, potentially solving complex problems that are currently impossible or take too long with regular computers. For investors, this technology could lead to breakthroughs in areas like cryptography, data analysis, and optimization, impacting financial markets and security systems.

hilbert space technical

"utilizing the multi-dimensional Hilbert space of quantum circuits to perform"

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.

parameterized quantum gates technical

"extracting complex features through parameterized quantum gates and entanglement"

Parameterized quantum gates are the adjustable building blocks of quantum circuits: like knobs on a machine, each gate has one or more settings that change how it manipulates quantum bits. Investors should care because these tunable gates let quantum devices be trained or optimized for practical tasks, enable near-term algorithms that can run on imperfect hardware, and therefore help translate lab experiments into products and measurable commercial progress.

entanglement structures technical

"through parameterized quantum gates and entanglement structures.Classical feature"

Entanglement structures are complex webs of ownership, contracts, loans, or shared directors that link separate companies so that problems in one can quickly affect the others. For investors, they matter because they make it harder to judge where risk and responsibility lie—like tangled ropes where pulling one strand tugs the rest—so troubles, debts or legal claims can spread and change a company’s value in unexpected ways.

convolutional layers technical

"taking the output of classical convolutional layers as input to quantum circuits"

Convolutional layers are building blocks in many artificial intelligence systems that scan data (like images, audio, or medical scans) with small, repeating windows to detect local patterns such as edges, textures or shapes. For investors, they matter because the quality and efficiency of these layers often determine how well a product recognizes signals, which affects a company’s competitive edge, development costs, speed to market and regulatory or safety performance in applications that rely on automated pattern recognition.

multi-qubit technical

"mapping image blocks to multi-qubit rotation angles so that they can"

Multi-qubit describes a system that uses multiple quantum bits (qubits) — the basic units of quantum information — working together to store and process data in ways classical bits cannot. Investors watch multi-qubit progress because more qubits, with low error rates and good connectivity, can unlock more powerful quantum calculations and commercial applications; think of it like moving from a single calculator to a team of coordinated computers tackling harder problems.

quantum state space technical

"angles so that they can represent complete local features in the quantum state space"

Quantum state space is the complete set of all possible conditions a tiny quantum system can be in, including the special combinations that give rise to quantum effects like superposition and entanglement. Think of it like a map of every position and setting a microscopic device can occupy; knowing that map matters to investors because it underpins how reliably and powerfully quantum technologies—computers, sensors, and communications—can perform, which shapes commercial potential and technical risk.

AI-generated analysis. Not financial advice.

BEIJING, Feb. 18, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, proposed a brand-new technological innovation—a hybrid quantum-classical Inception neural network model for image classification. This is a brand-new hybrid architecture that naturally integrates quantum computing with classical deep learning through Inception-style parallel feature channels, achieving triple improvements in performance, efficiency, and robustness. The core goal of this technology is to utilize the high-dimensional feature expression capability of quantum computing to solve the expressiveness bottleneck of image classification models, while enhancing engineering implementability through classical network structures, and building a research path regarding the relationship between quantum expressiveness, quantum entanglement degree, and model performance, laying the foundation for future hybrid quantum AI research.

Past quantum neural network research has mostly focused on constructing some kind of variational quantum circuit and attempting to embed it into traditional neural network structures. Although this method can achieve improvements in small-scale tasks, overall performance growth is slow and has not fully tapped the potential of quantum computing. For this reason, the WiMi research team realized that to allow quantum computing to play a true role in image classification, its parallel structure must be redesigned, especially needing to break through the structural limitations of single-path quantum networks.

The core idea of the Inception structure is to allow multiple sub-networks with different receptive fields and expression methods to extract features in parallel and then complete multi-scale fusion through concatenation. By re-examining the quantum-classical hybrid network through this idea, WiMi proposed three parallel feature paths:

Quantum feature extraction path: utilizing the multi-dimensional Hilbert space of quantum circuits to perform quantum encoding on local regions of images, and then extracting complex features through parameterized quantum gates and entanglement structures.

Classical feature extraction path: using efficient convolution and lightweight feature extraction units to enhance model stability and macroscopic structure recognition capabilities.

Hybrid quantum-classical path: taking the output of classical convolutional layers as input to quantum circuits, allowing classical features to be mapped into quantum space to obtain higher-order nonlinear expressive capabilities.

The three paths together constitute the parallel Inception module, and then the outputs are concatenated into the final feature tensor to enter the subsequent classifier.

Such a design not only enables the model to simultaneously possess three major advantages—quantum high-dimensional expression, classical strong stability, and cross-domain feature fusion—but also thoroughly solves the industry pain point of training difficulties caused by excessively deep circuits in pure quantum networks. The quantum part does not need to construct extremely deep circuits but instead achieves more expressive space in shallow layers through parallel structures, fundamentally improving model trainability and scalability.

The key to building a hybrid quantum-classical Inception network lies in how to effectively map image data to quantum circuits. WiMi adopted an encoding strategy based on parameterized rotation gates, mapping image blocks to multi-qubit rotation angles so that they can represent complete local features in the quantum state space. Subsequently, the team designed controlled rotation gates, entanglement structures, and depth-adapted quantum circuits to enable quantum states to achieve the highest possible expressiveness in limited depth.

The design of the quantum path follows the principles of shallow circuits, high entanglement, and strong expression. By introducing multiple sets of entanglement constructions, quantum states can rapidly diffuse between different layers and generate higher-order feature combinations. The structural selection of quantum circuits is no longer based on manual guessing but on systematic research into the relationships between expressiveness, entanglement degree, and training stability, thereby constructing the circuit topology most suitable for image classification.

The classical path adopts lightweight convolutional networks to maintain good generalization ability and training efficiency. In the hybrid path, WiMi embeds features extracted by classical convolutions into new quantum circuits for secondary enhancement, enabling the model to possess the capability of first classical understanding followed by quantum enhancement.

The entire Inception module provides the classifier with a richer, more three-dimensional, multi-scale feature expression space by concatenating and fusing features from the three paths. Among them, quantum features serve as high-order expression supplements, classical features are responsible for stable backbone expression, and the hybrid path acts as a bridge to naturally fuse the two.

Through extensive experimental validation, the WiMi research team discovered that the hybrid quantum-classical Inception structure has multiple outstanding advantages. The quantum path can capture highly complex texture variations and subtle patterns in images, the classical path ensures overall stability and robustness, and the hybrid path enables the model to possess cross-domain expressive capabilities. When combined, the model's performance in image classification tasks surpasses that of ordinary convolutional networks and single-path quantum networks, with particularly significant performance in scenarios with small data scales and subtle category differences. In addition, the high-dimensional nature of quantum circuits allows the model to achieve strong expressive power with fewer parameters, thereby realizing the dual advantages of high performance + low parameter count.

WiMi's hybrid quantum-classical Inception neural network is not merely a structural innovation; it represents a future trend: quantum computing will no longer exist as an independent model but will gradually become one of the foundational capabilities of deep learning. By deeply fusing quantum circuits with classical networks in terms of feature domains, information flows, and spatial structures, this model demonstrates a possible operation mode for future intelligent perception systems—quantum and classical parallel collaboration, processing features of different levels and natures in the most appropriate way. In the future, WiMi will continue to explore deeper hybrid structures, more complex quantum feature encoding methods, and deployment methods oriented toward real quantum hardware, promoting hybrid quantum artificial intelligence toward practical applications.

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.

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.

View original content:https://www.prnewswire.com/news-releases/wimi-studies-hybrid-quantum-classical-inception-neural-network-model-for-image-classification-302691534.html

SOURCE WiMi Hologram Cloud Inc.

FAQ

What is WiMi's hybrid quantum-classical Inception model announced on Feb 18, 2026 (WIMI)?

It is a three-path image classification architecture combining quantum, classical, and hybrid channels for feature extraction. According to the company, the model concatenates outputs from quantum encoding, lightweight convolutions, and quantum-enhanced classical features to form a richer final feature tensor.

How does the quantum path in WIMI's model encode image data for classification?

The quantum path maps image blocks to multi-qubit rotation angles using parameterized rotation gates for local feature encoding. According to the company, controlled rotations, entanglement structures, and depth-adapted circuits maximize expressiveness within shallow quantum layers.

What advantages does WiMi claim for its hybrid model versus classical convolutional networks (WIMI)?

WiMi claims improved performance, efficiency, and robustness, especially on small datasets and subtle category differences. According to the company, quantum high-dimensional features plus classical stability yield higher-order expression with fewer parameters than single-path models.

Will WIMI's hybrid Inception design require deep quantum circuits for training and deployment?

No; the design emphasizes shallow circuits with high entanglement rather than deep quantum layers to improve trainability. According to the company, parallel shallow quantum paths achieve expressive power while avoiding training difficulties of deep quantum networks.

How does the hybrid path in the WIMI model combine classical and quantum features?

The hybrid path feeds classical convolution outputs into quantum circuits for secondary enhancement and cross-domain fusion. According to the company, this enables first classical understanding followed by quantum enhancement to produce higher-order nonlinear features.

What are WiMi's next steps for the hybrid quantum-classical Inception model (WIMI)?

WiMi plans to explore deeper hybrid structures, more complex quantum encodings, and deployment on real quantum hardware. According to the company, future work will focus on practical hybrid quantum AI applications and improved hardware-oriented methods.