WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

Rhea-AI Summary

WiMi (NASDAQ: WIMI) on January 5, 2026 announced MC-QCNN, a next-generation Quantum Convolutional Neural Network for Multi-Channel Supervised Learning. The company says the design creates hardware-adaptable quantum convolution kernels that encode multi-channel data into amplitudes, phases, or entanglement and use parameterized gates, SWAP interleaving, weak entanglement, and learnable quantum pooling to preserve features.

WiMi describes a hybrid quantum-classical training framework, extended parameter-shift training, noise simulation, and claimed advantages for image classification, medical imaging, video analysis, and multimodal monitoring.

News Market Reaction

+10.89%

13 alerts

+10.89% News Effect

+8.4% Peak in 3 hr 6 min

+$3M Valuation Impact

$28M Market Cap

1.4x Rel. Volume

On the day this news was published, WIMI gained 10.89%, reflecting a significant positive market reaction. Argus tracked a peak move of +8.4% during that session. Our momentum scanner triggered 13 alerts that day, indicating notable trading interest and price volatility. This price movement added approximately $3M to the company's valuation, bringing the market cap to $28M at that time.

Data tracked by StockTitan Argus on the day of publication.

Key Figures

Convertible note size: $35 million Purchase price: $32.2 million Original-issue discount: 8% +5 more

8 metrics

Convertible note size $35 million Unsecured convertible note purchased from MicroAlgo on June 20, 2025

Purchase price $32.2 million Cash paid by WiMi for $35M MicroAlgo convertible note

Original-issue discount 8% Discount on MicroAlgo convertible note relative to face value

Immediate paper gain $2.8 million Difference between $35M face and $32.2M purchase price

Note maturity 360 days Term of MicroAlgo unsecured convertible note

Conversion discount 60% Discount to lowest 60-day closing price for MicroAlgo conversion shares

Lock-up period 10 years Lock-up on any MicroAlgo conversion shares from the note

Price vs 52-week high -90.88% WIMI relative to its 52-week high before MC-QCNN news

Market Reality Check

Price: $2.82 Vol: Volume 241,904 is 2.67x t...

high vol

$2.82 Last Close

Volume Volume 241,904 is 2.67x the 20-day average of 90,650, indicating elevated interest ahead of this AI release. high

Technical Shares at $2.48 trade below the $3.99 200-day MA and sit 90.88% under the 52-week high, but 10.96% above the 52-week low.

Peers on Argus

WIMI was up 4.64% while momentum peers like KRKR and CHR showed declines of 12.1...

2 Down

WIMI was up 4.64% while momentum peers like KRKR and CHR showed declines of 12.18% and 6.29%, suggesting a stock-specific reaction rather than a Communication Services sector move.

Historical Context

5 past events · Latest: Dec 22 (Positive)

Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Dec 22	AI tech launch	Positive	-1.4%	Unveiled hybrid quantum neural network for advanced image multi-classification.
Dec 22	AI tech launch	Positive	-1.4%	Repeat entry: hybrid quantum neural network announcement with similar details.
Dec 04	Quantum-classical R&D	Positive	+1.9%	Presented hybrid quantum-classical architecture to reduce pooling information loss.
Dec 04	Quantum-classical R&D	Positive	+1.9%	Duplicate record of multi-class image classification hybrid architecture.
Nov 20	QGAN architecture	Positive	-5.6%	Introduced dual-discriminator QGAN design to improve training efficiency and stability.

Pattern Detected

Recent tech-heavy AI announcements have produced mixed reactions: 2 aligned positive moves and 3 divergences, indicating that quantum/AI R&D news does not consistently translate into upside.

Recent Company History

Over the last few months, WiMi has repeatedly highlighted quantum and hybrid AI advances. On Oct 15, 2025, it introduced a Quantum Semi-Supervised Learning framework with no immediate price move. Later AI-tag releases on Oct 20 and Oct 23, 2025 around SQ-QNN and SHQCNN saw moves of 2.07% and 5.9%. The Dec 22, 2025 H-QNN launch, however, drew a -1.37% reaction. Today’s MC-QCNN update extends this quantum AI trajectory.

Market Pulse Summary

The stock surged +10.9% in the session following this news. A strong positive reaction aligns with W...

Analysis

The stock surged +10.9% in the session following this news. A strong positive reaction aligns with WiMi’s pattern of sizable moves around AI-tagged releases, where past 1-day changes reached up to 5.9%. However, mixed outcomes—such as the -1.37% move after the H-QNN launch—show that enthusiasm around quantum R&D has not always persisted. Investors have previously reacted both favorably and unfavorably to similar advances, so any sharp upside could reverse if commercialization progress lags prior technical claims.

Key Terms

quantum convolutional neural network, qubit, quantum decoherence, quantum entanglement, +4 more

8 terms

quantum convolutional neural network technical

"a Quantum Convolutional Neural Network for Multi-Channel Supervised Learning (MC-QCNN)."

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.

qubit technical

"including convolution kernel structure, qubit layout, channel interaction encoding,"

A qubit is the basic unit of information used in quantum computers, like a coin that can be heads, tails or both at once until you look; this lets quantum machines process many possibilities simultaneously. For investors, qubits matter because their number, quality and stability determine how powerful a quantum computer can be, affecting which companies might gain an edge in fields such as cryptography, drug discovery, materials design or complex financial modeling.

quantum decoherence technical

"while maintaining robustness against quantum decoherence."

Quantum decoherence is the process by which a tiny quantum system loses its delicate, particle-like and wave-like behavior when it interacts with its noisy surroundings, like a spinning top that wobbles and falls over when bumped. For investors, decoherence matters because it is the main technical hurdle that makes quantum computers, sensors and communications fragile and expensive; how quickly companies suppress decoherence affects timelines, costs and competitive advantage in commercializing quantum technologies.

quantum entanglement medical

"through gate-level interactions, producing stronger feature combination capabilities than classical convolution."

Quantum entanglement is a phenomenon where two or more particles become linked in such a way that the state of one instantly influences the state of the other, no matter how far apart they are. For investors, understanding entanglement highlights how new, highly interconnected technologies could disrupt traditional markets by enabling instantaneous sharing of information or capabilities across distances, potentially creating new opportunities or risks.

quantum pooling technical

"downsampled by quantum pooling circuits. The pooling circuits have also been redesigned"

Quantum pooling is a method that combines information from multiple sources to make more accurate predictions or decisions. Imagine gathering different pieces of a puzzle to see the full picture more clearly—this process helps investors better understand risks and opportunities by integrating diverse data. It matters because it can improve the precision of forecasts and enhance strategic choices in financial markets.

gradient clipping technical

"introduces quantum noise simulation and gradient clipping mechanisms, ensuring that the model's"

Gradient clipping is a technique used when training machine learning models that puts a cap on how large a single learning step can be, preventing sudden, runaway changes that can break training. For investors, it matters because it helps teams train models more reliably and efficiently, reducing the risk of costly failures, longer development times, or wasted compute — think of it like limiting how sharply you can turn a steering wheel so a car doesn’t skid off course.

convertible note financial

"disclose a $35 million unsecured convertible note purchase from its majority-owned"

A convertible note is a type of loan that a company gets from investors, which can later be turned into company shares instead of being paid back in cash. It matters because it helps startups raise money quickly without setting a fixed value for the company right away, making it easier to grow and attract investors.

lock-up agreement regulatory

"WiMi entered a parallel 10-year Lock-Up Agreement prohibiting the sale or transfer"

A lock-up agreement is a contract that prevents company insiders and early investors from selling their shares for a fixed period after a stock sale, often after an initial public offering. It matters to investors because it temporarily limits the number of shares that can hit the market, which can keep the share price steadier; when the lock-up ends, a sudden increase in available shares can create extra volatility, revealing insiders’ confidence or lack thereof.

AI-generated analysis. Not financial advice.

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

BEIJING, Jan. 05, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the launch of their independently developed new technology: a Quantum Convolutional Neural Network for Multi-Channel Supervised Learning (MC-QCNN). This breakthrough method, for the first time, constructs a fully hardware-adaptable quantum convolution kernel design, enabling quantum models to efficiently process multi-channel data, thereby demonstrating absolute advantages in industries such as image classification, medical imaging, video analysis, and multimodal monitoring.
From a research and development perspective, the core of this technological breakthrough lies not merely in the construction of multi-channel quantum convolution kernels but in the entire systematized design scheme, including convolution kernel structure, qubit layout, channel interaction encoding, weight learnability, interpretability, and hardware constraint adaptation strategies. To enable the technology to be executed on real hardware, WiMi abandoned a large number of impractical deep circuit structures and instead turned to a design philosophy that is closer to the native gate operation characteristics of quantum hardware. The quantum circuit convolution kernel proposed by WiMi uses single-bit rotation gates, controlled parameterized gates, SWAP interleaving structures, weak entanglement layers, and channel interaction gates, thereby forming a convolution operator that can express complex functions while maintaining robustness against quantum decoherence.
Unlike classical convolution kernels that need to slide within pixel neighborhoods, WiMi adopted a quantum-specific encoding method to compress and encode data from multiple channels into the amplitudes, phases, or entanglement structures of quantum states, performing convolution-like processing on them through parameterized quantum gates. Feature fusion between channels no longer relies on linear weighting but directly generates high-dimensional correlations in the quantum state space through gate-level interactions, producing stronger feature combination capabilities than classical convolution. Through training, these parameterized quantum convolution kernels can learn high-order cross-channel features, such as texture-color co-occurrence, time-space joint patterns, multispectral energy distribution correlations, etc., thereby achieving expressive capabilities superior to traditional QCNN.
One of the cores of this technology architecture is the quantum multi-channel convolution operator established by WiMi. This operator uses parameterized rotation gates and controlled gates to construct convolution patterns. By adjusting the rotation angles of the gates and the controlled structures, the convolution kernel can automatically learn the optimal cross-channel feature combination strategy during training. The entire convolution kernel can not only act on single-bit distributions but also act on multi-bit channel structures in a tensor-like manner, enabling the convolution kernel not only to extract local coherence but also to mine high-order relationships from entanglement structures. This mode cannot be directly realized in classical CNNs because the combination of multi-channel features in classical neural networks is usually based on linear superposition, whereas quantum convolution kernels are based on quantum superposition and quantum entanglement, capable of expressing complex multi-channel correlations in an exponential feature space.
After the convolution operation is completed, the feature maps are compressed into more compact quantum states in the quantum system and downsampled by quantum pooling circuits. The pooling circuits have also been redesigned to handle quantum state features from multiple channels. WiMi adopts a learnable quantum pooling mode, reducing quantum state dimensions through controllable measurements or controllable compression operations while preserving key feature information, which avoids the feature destruction problem caused by direct measurements in traditional QCNNs. Experimental results show that the new pooling structure is more stable than traditional QCNN pooling methods and has a higher feature retention rate.
In addition to convolution kernels and pooling circuits, WiMi has also constructed a dedicated hybrid quantum-classical training framework. During the training process, the classical computing module is responsible for loss function calculation, gradient solving, and parameter updating, while the quantum module is responsible for forward propagation and quantum state evolution. WiMi adopts an extended parameter shift rule approach, enabling all parameters in the multi-channel quantum convolution kernel to be effectively trained. To improve training stability, WiMi also introduces quantum noise simulation and gradient clipping mechanisms, ensuring that the model's performance on real quantum hardware does not sharply decline due to noise.
During the training process, the WiMi team observed a highly valuable phenomenon: the model is able to automatically capture nonlinear correlations between multiple channels. Taking RGB images as an example, the quantum convolution kernels learned by the model do not simply perform linear traversal on the R, G, and B channels but instead establish correlations between channels through entanglement layers, enabling the convolution kernel to recognize joint features of color distribution patterns in the quantum state space. This means that the model is not performing convolution separately on the three channels but is learning an overall deep feature in a higher-dimensional space, with expressive power far superior to that of 3×3 or 1×1 convolutions in classical CNNs.
WiMi believes that multi-channel processing capability will become one of the key abilities for quantum neural networks to move toward real-world applications. Although single-channel QCNN has exploratory significance in academia, its limitations make it unable to meet the industry's requirements for complex data. The emergence of MC-QCNN enables quantum deep learning systems to possess the ability to process real-world data for the first time, meaning that quantum AI is no longer just a laboratory concept but is beginning to have the possibility of commercial implementation. It is believed that, with the improvement of quantum hardware performance, this technology will drive quantum machine learning from laboratory research toward a true era of applications.
In the future, WiMi will continue to refine this technology system, including building more efficient quantum convolution kernel structures, developing more robust noise adaptation strategies, extending to three-dimensional convolution and time-series convolution structures, and exploring integration possibilities with model structures such as Transformer, enabling quantum models to process not only multi-channel images but also multimodal speech, video, text, graph structures, and sensor data. Quantum deep learning will no longer be limited to small-scale tasks but will become an important operator in next-generation general AI models. The combination of quantum computing and artificial intelligence will be the core trend in technological development over the next decade. WiMi will continue to dedicate itself to promoting the construction of the quantum AI ecosystem, allowing quantum technology to truly serve industrial needs, social value, and the human future.

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.

Investor Inquiries, please contact:

WIMI Hologram Cloud Inc.
Email: pr@wimiar.com

ICR, LLC
Robin Yang
Tel: +1 (646) 975-9495
Email: wimi@icrinc.com

FAQ

What did WiMi announce on January 5, 2026 about MC-QCNN (WIMI)?

WiMi announced a Multi-Channel Quantum Convolutional Neural Network (MC-QCNN) intended to process multi-channel data on hardware-adaptable quantum circuits.

How does WiMi say MC-QCNN handles multi-channel data for WIMI applications?

MC-QCNN encodes multi-channel data into quantum amplitudes, phases, or entanglement and uses parameterized gates and channel-interaction gates to learn cross-channel features.

Will MC-QCNN run on real quantum hardware according to WiMi (WIMI)?

WiMi states the design favors native gate operations, uses SWAP interleaving and weak entanglement, and includes noise simulation to improve performance on real hardware.

What training approach did WiMi describe for MC-QCNN (WIMI)?

WiMi describes a hybrid quantum-classical framework using a classical module for loss and gradients and an extended parameter-shift rule to train quantum convolution parameters.

Which industries did WiMi highlight as potential early beneficiaries of MC-QCNN (WIMI)?

WiMi cited image classification, medical imaging, video analysis, and multimodal monitoring as areas that may benefit from MC-QCNN.

What future developments did WiMi indicate for its quantum AI roadmap (WIMI)?

WiMi said it will refine kernels, develop noise adaptation, extend to 3D and time-series convolutions, and explore integration with Transformer-like models.