WiMi Explores Multi-Dimensional Pooling Optimization Technology under the Variational Quantum Algorithm Framework

WiMi (NASDAQ: WIMI) is researching a multi-dimensional pooling optimization technology within a variational quantum algorithm (VQA) framework.

Rhea-AI Summary

WiMi (NASDAQ: WIMI) is researching a multi-dimensional pooling optimization technology within a variational quantum algorithm (VQA) framework. The approach combines the Quantum Haar Transform (QHT) with quantum partial measurement to preserve local features while compressing dimensions in high-dimensional data.

The VQA-based method aims to enable richer feature representation, polynomial-level computational acceleration, and scalability across audio, images, point clouds, and hyperspectral data, supporting future quantum machine learning applications.

Argus Jun 8 session

+1.27% close to close Open Argus

Details

News Market Reaction – WIMI

In the Jun 8 session, WIMI gained 1.27%, reflecting a mild positive market reaction.

Data tracked by StockTitan Argus on the day of publication.

Market Context

This announcement adds another quantum machine learning initiative to WiMi’s recent stream of R&D, e...

Analysis

This announcement adds another quantum machine learning initiative to WiMi’s recent stream of R&D, extending prior work in quantum CNNs and optimization into multi-dimensional pooling under a VQA framework. Against a backdrop of improved 2025 profitability, higher working capital above RMB 2.6B, and recent board changes, investors may track how often these quantum capabilities translate into commercial products, as well as ongoing regulatory and structural risks highlighted in the latest 20-F and 6-K filings.

Key Figures

Net income 2025: RMB 347.1M (USD 49.4M) Net income growth: RMB 243.8M, 235.9% increase Operating expenses 2025: RMB 147.6M (USD 21.0M) +5 more

Net income 2025

RMB 347.1M (USD 49.4M)

Form 20-F / 6-K year ended Dec 31, 2025

Net income growth

RMB 243.8M, 235.9% increase

Versus 2024, per 6-K on annual results

Operating expenses 2025

RMB 147.6M (USD 21.0M)

Year ended Dec 31, 2025, 6-K filing

Opex change

19.4% decrease from RMB 183.1M

Cost control vs prior year in 6-K

Working capital

RMB 2,611.6M (USD 371.6M)

As of Dec 31, 2025, per 6-K

Working capital growth

Up 105.8% from RMB 1,269.2M

Year-over-year change in liquidity

ADS conversion ratio

1 ADS = 2 Class B shares

Form 20-F description of ADS program termination

Share consolidation

20-to-1 consolidation

Corporate action disclosed in Form 20-F

Historical Context

5 past events · Latest: Jun 02

5 events

1. Jun 02

   Quantum architecture R&D

   24h Move

   -2.3%

   Proposed fault-tolerant quantum computing architecture using multi-hypercube codes.

2. May 28

   Quantum AI progress

   24h Move

   +4.8%

   Breakthrough in quantum deep CNN for image recognition using parameterized circuits.

3. May 21

   Quantum optimization R&D

   24h Move

   +1.9%

   Quantum computing optimization with multi-objective deep reinforcement learning.

4. May 11

   Quantum feature mapping

   24h Move

   -1.3%

   Release of repeated amplitude encoding to enhance quantum model expressiveness.

5. May 06

   Quantum NLP model

   24h Move

   +6.3%

   Multi-scale fusion quantum CNN for text classification with accuracy and parameter gains.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Key Terms

haar transform, quantum entanglement, variational quantum algorithm, parameterized quantum circuit, +3 more

7 terms

haar transform technical

"the Haar transform, as a core technology in the field of classical signal"

The Haar transform is a mathematical tool that breaks a series of numbers into simple building blocks that capture both broad trends and sudden changes, similar to viewing a picture at different resolutions. Investors use it to clean noisy price or volume data, highlight abrupt shifts or short-term patterns, compress information for faster models, and improve algorithmic signals for trading, risk monitoring, or data-driven decisions.

quantum entanglement technical

"correlations between feature dimensions are constructed through quantum entanglement,"

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.

variational quantum algorithm technical

"pooling optimization technology under the variational quantum algorithm framework,"

A variational quantum algorithm is a practical approach to solving hard problems by letting a quantum device try different settings while a regular computer evaluates and guides those choices in a repeated loop, like tuning knobs on a machine until performance improves. Investors should care because these hybrid methods aim to unlock faster solutions for optimization, simulation, and machine learning tasks that can reduce costs or create competitive advantage if quantum hardware matures.

parameterized quantum circuit technical

"Its core architecture consists of a parameterized quantum circuit (PQC) and a"

A parameterized quantum circuit is a small, controllable program for a quantum computer made of basic operations whose strengths can be adjusted like knobs. Investors should care because these adjustable circuits are the working pieces behind many proposed quantum algorithms for optimization, simulation and machine learning; they are where researchers test whether quantum hardware can eventually solve certain financial or computational problems faster than classical computers, making them a focal point for early-stage technology value and risk.

quantum partial measurement technical

"QHT with quantum partial measurement, and constructing a quantum pooling mechanism"

A quantum partial measurement is a way to extract limited information from a quantum system without forcing it into a single, fully determined state; imagine gently peeking under a covered coin to learn if it might be heads without flipping it over. For investors, this technique matters because it can reduce errors, enable repeated checks and more efficient control in quantum computers and sensors, potentially speeding commercialization and improving the value of technologies that rely on reliable quantum behavior.

quantum superposition technical

"second, leveraging the characteristics of quantum superposition and entanglement to"

Quantum superposition is a property of tiny particles where a single object can exist in multiple possible states at the same time until it is measured; think of it as a coin spinning so fast it is both heads and tails until you stop it. For investors, superposition is the key principle that gives quantum computers their potential to solve certain problems far faster than conventional machines, which can reshape industries, change competitive advantages and influence the value of tech and cybersecurity investments.

quantum parallelism technical

"third, relying on quantum parallelism to significantly reduce the computational"

Quantum parallelism is a computing property that lets a quantum processor explore many possible answers at once by holding data in overlapping states, instead of testing options one after another. For investors it matters because that simultaneous processing can radically speed up tasks such as drug discovery, complex optimization, or code cracking, potentially reshaping competitiveness, risk and valuation in tech, biotech and cybersecurity—think of scanning an entire shopping cart in a blink rather than scanning items one by one.

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BEIJING, June 8, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, is exploring multi-dimensional pooling optimization technology under the variational quantum algorithm framework, proposing an innovative solution that integrates the Quantum Haar Transform (QHT) with quantum partial measurement, and constructing a quantum pooling mechanism that possesses both local feature preservation capability and dimension compression efficiency.

From a technical principle perspective, the Haar transform, as a core technology in the field of classical signal processing, is widely used in data compression and feature extraction. As its quantized extension, QHT maps high-dimensional classical data to the quantum state space through parameterized quantum gate groups, achieving a breakthrough improvement in computational efficiency over the classical Haar transform. In this mapping process, each qubit corresponds to one feature dimension of the data, and the superposition coefficients of the quantum state encode the feature intensity information. At the same time, correlations between feature dimensions are constructed through quantum entanglement, which not only fully preserves the global structural information of the data but also reinforces the correlations of local features through the local action domain constraints of quantum gates, effectively solving the problem of exponentially increasing computational complexity that the classical Haar transform faces in high-dimensional data processing. After QHT completes the data mapping, quantum partial measurement technology undertakes the core function of multi-dimensional data pooling. Its core logic differs from the crude dimensionality reduction mode of traditional pooling that directly discards redundant data, instead utilizing the probabilistic characteristics of quantum states combined with preset pooling strategies to selectively extract key feature information from quantum states in probabilistic form.

As the core driver of the entire optimization scheme, VQA constructs a hybrid optimization framework by integrating quantum computing and classical optimization technologies. Its core architecture consists of a parameterized quantum circuit (PQC) and a classical optimizer. By iteratively adjusting the parameters of the quantum circuit to minimize a preset loss function, it ensures that the pooling operation can accurately capture the key features of high-dimensional data while balancing computational efficiency and precision. In multi-dimensional pooling optimization scenarios, the core value of VQA is reflected in three aspects: first, realizing direct pooling of multi-dimensional data without the need to reduce high-dimensional data to one-dimensional space, fundamentally solving the problem of local feature loss caused by traditional pooling and fully preserving the spatial structure and local correlations of the data; second, leveraging the characteristics of quantum superposition and entanglement to obtain richer feature representations of multi-dimensional data in the quantum state space, enabling the extraction of fine and complex features that classical pooling methods cannot capture; third, relying on quantum parallelism to significantly reduce the computational complexity of high-dimensional data pooling, achieving polynomial-level computational acceleration and substantially improving model training and inference efficiency. In addition, the VQA framework possesses good scalability. By adjusting the parameters and gate structures of the quantum circuit, it can flexibly adapt to the processing needs of unstructured data of different dimensions and types, such as one-dimensional audio, two-dimensional images, three-dimensional point clouds, and hyperspectral data, demonstrating broad application prospects.

The VQA-driven multi-dimensional pooling optimization technology researched by WiMi will break through the locality preservation limitations of traditional pooling methods in high-dimensional data processing, fully unleash the inherent advantages of quantum computing in feature representation and computational efficiency, and provide key technical support for the practical application of QML in complex multi-dimensional data tasks.

In the future, with the iterative upgrading of quantum hardware and the continuous optimization of algorithms, the multi-dimensional pooling optimization technology under the VQA framework is expected to achieve practical application in more fields.

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-explores-multi-dimensional-pooling-optimization-technology-under-the-variational-quantum-algorithm-framework-302793940.html

SOURCE WiMi Hologram Cloud Inc.

FAQ

What quantum technology is WiMi (NASDAQ: WIMI) developing in June 2026?

WiMi is developing multi-dimensional pooling optimization technology under a variational quantum algorithm (VQA) framework. According to WiMi, it integrates the Quantum Haar Transform and quantum partial measurement to preserve local features while compressing dimensions in complex, high-dimensional data for quantum machine learning.

How does WiMi's Quantum Haar Transform help high-dimensional data processing for WIMI?

WiMi’s Quantum Haar Transform (QHT) maps high-dimensional classical data into quantum state space via parameterized quantum gates. According to WiMi, this leverages superposition and entanglement to encode feature intensity and correlations, addressing exponential complexity issues seen in classical Haar transforms on large datasets.

What role does the variational quantum algorithm (VQA) play in WiMi's pooling optimization?

The VQA provides a hybrid quantum-classical optimization loop for pooling. According to WiMi, a parameterized quantum circuit and classical optimizer iteratively minimize a loss function so pooling captures key high-dimensional features while balancing computational efficiency and precision in quantum machine learning tasks.

How is WiMi's quantum pooling different from traditional pooling methods?

WiMi’s quantum pooling uses probabilistic quantum partial measurements instead of simply discarding data. According to WiMi, it allows direct pooling of multi-dimensional data, better preserves spatial structure and local correlations, and can extract fine, complex features that classical pooling methods may miss.

What applications could WiMi's VQA-based pooling technology have for WIMI investors?

WiMi expects its VQA-based pooling to support quantum machine learning on varied data types. According to WiMi, the method can adapt to one-dimensional audio, two-dimensional images, three-dimensional point clouds, and hyperspectral data, aiming at broader practical use as quantum hardware and algorithms advance.

Does WiMi provide a timeline for commercial use of its VQA pooling technology?

WiMi does not give a specific commercialization timeline for its VQA pooling technology. According to WiMi, practical applications are expected to expand in more fields as quantum hardware iterates and related algorithms continue to be optimized over time.