WIMI - WiMi Develops Scalable Quantum Neural Network (SQNN) Technology for Collaborative Quantum Computing

2025-09-19 16:01:08 ET

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) , a global leader in Hologram Augmented Reality (AR) technology, has announced the development of a groundbreaking Scalable Quantum Neural Network (SQNN) technology. This innovative system leverages multi-quantum-device collaborative computing to enhance the efficiency and scalability of quantum neural networks.

The SQNN technology utilizes multiple small quantum devices as quantum feature extractors, which work in parallel to process input data. These extracted features are then aggregated through classical communication channels to perform final classification tasks. This approach addresses the limitations of current quantum computing hardware by enabling collaborative functionality across multiple devices, optimizing resource utilization, and improving data efficiency.

Core Components of SQNN

WiMi's SQNN system is built on three primary components:

Quantum Feature Extractor : Each quantum device independently extracts local features from input data using Variational Quantum Circuits (VQC). This modular design allows devices of varying sizes to handle different levels of data complexity, with larger devices managing intricate patterns and smaller ones focusing on simpler features.

Classical Communication Channel : Extracted features are transmitted to a central computing node via classical communication channels. This process mirrors Federated Learning, where independent computing units collaborate to integrate global information for decision-making.

Quantum Predictor : Acting as the system's core computational unit, the quantum predictor aggregates feature information and performs final classification tasks using advanced quantum circuits. It dynamically adjusts its computational approach based on the scale and complexity of the data.

Technical Implementation

The implementation of SQNN involves several key steps:

• Data Preprocessing and Quantum Encoding : Input data undergoes classical preprocessing, such as standardization and dimensionality reduction, before being mapped to quantum states using encoding methods like Amplitude Encoding or Angle Encoding.
• Sub-feature Extraction : Parameterized Quantum Circuits (PQC) are employed by each quantum device to extract and transform local features.
• Feature Aggregation and Classification : Extracted features are transmitted to a central node, where the quantum predictor aggregates them and performs classification tasks.
• Parameter Optimization and Training : Variational Quantum Optimization is used for training, with classical optimizers like gradient descent adjusting quantum circuit parameters to minimize classification errors.

Advantages of SQNN

WiMi's SQNN offers several significant advantages over traditional Quantum Neural Networks (QNNs):

• Improved Data Utilization : By leveraging multiple quantum devices, SQNN efficiently processes data without being constrained by the qubit limitations of a single device.
• Enhanced Computational Scale : The collaborative approach enables SQNN to handle larger-scale tasks without relying on a single high-performance quantum computer, making it highly scalable.
• Optimized Resource Allocation : SQNN allows flexible resource management, activating only the necessary quantum devices based on workload demands.

Experimental Results and Scalability

Experiments on benchmark datasets demonstrate that SQNN achieves classification accuracy comparable to traditional QNNs while significantly improving training efficiency. As the number of participating quantum devices increases, both classification accuracy and computation speed improve, highlighting the system's scalability.

Despite these promising results, challenges remain, such as optimizing inter-device communication to reduce costs and refining quantum circuit designs to minimize noise interference and enhance accuracy.

WiMi's SQNN technology represents a major step forward in quantum machine learning, offering a scalable and efficient solution for collaborative quantum computing. As quantum hardware continues to evolve, SQNN is poised to become a cornerstone of large-scale quantum machine learning systems, driving transformative advancements in artificial intelligence and data science.

About WiMi Hologram Cloud

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) is a leading provider of holographic cloud solutions, specializing in areas such as holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, and holographic semiconductor technology. The company's services span holographic AR advertising, entertainment, automotive applications, and interactive communication, among other cutting-edge technologies.

WiMi's commitment to innovation continues to position it as a global leader in holographic AR and quantum computing technologies.

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