WiMi Releases Next-Generation Quantum Neural Network Feature Mapping Technology: Repeated Amplitude Encoding Significantly Enhances Expressive Power of Quantum Models

WiMi (NASDAQ: WIMI) released a next-generation quantum neural network feature mapping technology called Repeated Amplitude Encoding (RAE).

Rhea-AI Summary

WiMi (NASDAQ: WIMI) released a next-generation quantum neural network feature mapping technology called Repeated Amplitude Encoding (RAE). RAE repeatedly encodes the same classical data across multiple qubit blocks to enhance mapping to complex feature spaces while keeping quantum resource usage controllable.

According to WiMi, experiments on the MNIST image classification dataset show that, at a fixed number of classes, quantum neural networks using RAE achieved higher classification accuracy, better convergence stability, and stronger robustness to parameter initialization than traditional amplitude and angle encoding methods.

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News Market Reaction – WIMI

On May 11, the day this news came out, WIMI closed 1.25% below the previous close.

Data tracked by StockTitan Argus for the May 11 session.

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

Key Terms

quantum neural networks, amplitude encoding, qubit, mnist, +1 more

5 terms

quantum neural networks technical

"released a key foundational technology oriented toward quantum neural networks—the Repeated"

Quantum neural networks are computing models that combine ideas from quantum mechanics with the structure of artificial neural networks to process information in fundamentally different ways than ordinary computers. For investors, they matter because the technology promises potentially much faster or more powerful machine learning for tasks like pattern recognition and optimization, but it also carries high technical uncertainty and long timelines, making related investments speculative and high-risk.

amplitude encoding technical

"the Repeated Amplitude Encoding method (Repeated Amplitude Encoding, RAE). This technology"

A method from quantum computing that stores a list of classical numbers by turning them into the strengths (amplitudes) of a quantum state so many values can be represented using far fewer physical bits. For investors, it matters because this packing can make quantum algorithms dramatically faster or more compact for certain problems, so claims about amplitude encoding affect the realistic performance, cost and scalability of quantum hardware and software.

qubit technical

"classical data across multiple qubit blocks, thereby providing an entirely new engineered"

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.

mnist technical

"used the classic image classification benchmark dataset MNIST as the experimental platform"

MNIST is a widely used collection of 70,000 small black-and-white images of handwritten digits that researchers use to train and test basic image-recognition algorithms. For investors, it matters because success on MNIST indicates a model can solve simple pattern-recognition tasks but does not prove real-world performance; think of it as a company showing flashcard practice rather than field-ready results, so it helps gauge early-stage AI claims but not final product capability.

classification accuracy technical

"outperform the control methods in classification accuracy, convergence stability, and robustness"

Classification accuracy is the share of cases a predictive system labels correctly — for example, how often an algorithm sorts items into the right category or a diagnostic test gives the right result. Investors care because higher accuracy generally means the product, model or test is more reliable, which can affect customer trust, regulatory approval, revenue potential and operational risk; think of it as a report card grade for how well a tool makes decisions.

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BEIJING, May 11, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, released a key foundational technology oriented toward quantum neural networks—the Repeated Amplitude Encoding method (Repeated Amplitude Encoding, RAE). This technology effectively enhances the mapping capability of quantum neural networks to complex feature spaces by performing repeated amplitude encoding of the same set of classical data across multiple qubit blocks, thereby providing an entirely new engineered path for constructing quantum neural network models that possess high expressive power while maintaining controllable resource usage.

From a technical background perspective, existing mainstream quantum neural networks generally rely on parameterized quantum gates to encode input data during the feature mapping stage. These quantum gates are mathematically linear or unitary transformations in essence, and the feature mappings formed by their combinations are often limited by circuit depth, the number of qubits, and the scale of trainable parameters. Although the quantum state itself resides in an exponentially high-dimensional space, in practical models, the limited encoding methods make it difficult to fully unleash this high-dimensional advantage, resulting in issues such as insufficient mapping capability and weak category scalability in complex classification tasks.

To address the above bottlenecks, WiMi, starting from the fundamental mechanism of quantum state representation, re-examined the way classical data enters the quantum system. The traditional amplitude encoding method typically maps a set of normalized classical feature vectors into the probability amplitudes of a single quantum state. Its advantage lies in high qubit usage efficiency, but the disadvantage is that the feature distribution after a single encoding is easily diluted by linear operations during the evolution of the quantum circuit, thereby limiting the ability of subsequent quantum neural networks to model complex nonlinear structures.

To verify the effectiveness of this technology in real tasks, WiMi used the classic image classification benchmark dataset MNIST as the experimental platform and conducted a systematic evaluation of the repeated amplitude encoding method. In the experiments, researchers embedded this method into various typical quantum neural network architectures and compared it with mainstream data loading methods such as traditional amplitude encoding and angle encoding.

The experimental results show that, under the condition of a fixed number of classes, quantum neural networks adopting repeated amplitude encoding outperform the control methods in classification accuracy, convergence stability, and robustness to parameter initialization. This indicates that, even under the same task complexity, repeated amplitude encoding can provide the model with more discriminative feature representations.

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.

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SOURCE WiMi Hologram Cloud Inc.

FAQ

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What is WiMi (NASDAQ: WIMI) Repeated Amplitude Encoding technology announced on May 11, 2026?

Repeated Amplitude Encoding (RAE) is a feature mapping method for quantum neural networks that repeatedly encodes the same classical data across multiple qubit blocks. According to WiMi, RAE aims to enhance expressive power while controlling quantum resource usage in complex tasks.

How does WiMi Repeated Amplitude Encoding improve quantum neural network performance compared with traditional methods?

WiMi reports that quantum neural networks using Repeated Amplitude Encoding outperform traditional amplitude and angle encoding in classification accuracy, convergence stability, and robustness to parameter initialization. According to WiMi, this performance gain appears under the condition of a fixed number of classes on tested tasks.

What problem in quantum neural network feature mapping does WiMi Repeated Amplitude Encoding target?

Repeated Amplitude Encoding targets limitations from linear, parameterized quantum gates and single encoding schemes that can dilute feature distributions. According to WiMi, RAE seeks to better exploit high-dimensional quantum states to address insufficient mapping capability and weak category scalability in complex classification tasks.

How did WiMi validate its Repeated Amplitude Encoding on the MNIST dataset?

WiMi tested Repeated Amplitude Encoding on the MNIST image classification benchmark, embedding it into several typical quantum neural network architectures. According to WiMi, systematic comparisons with traditional amplitude encoding and angle encoding showed performance advantages for RAE at a fixed number of classes.

What are the key technical characteristics of WiMi Repeated Amplitude Encoding for quantum models?

Repeated Amplitude Encoding repeatedly loads normalized classical feature vectors into multiple qubit blocks instead of a single quantum state. According to WiMi, this approach aims to produce more discriminative feature representations while maintaining controllable circuit depth, qubit counts, and trainable parameter scales.