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.

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