WiMi Hologram Cloud develops QFNN algorithm
Written by Emily J. Thompson, Senior Investment Analyst
Updated: Apr 23 2025
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WiMi's Quantum Computing Innovation: WiMi Hologram Cloud has developed a Quantum Computing-Based Feedforward Neural Network (QFNN) algorithm that enhances the training of neural networks by efficiently approximating inner products and utilizing Quantum Random Access Memory for rapid data retrieval, resulting in exponential speedup during both feedforward propagation and backpropagation processes.
Advantages of Quantum Techniques: The QFNN algorithm leverages quantum state superposition for matrix-vector operations and employs the Quantum Fourier Transform to accelerate gradient calculations, significantly reducing computational load and time compared to traditional methods.
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About WIMI
WiMi Hologram Cloud Inc is a holding company principally engaged in the provision of augmented reality (AR)-based holographic services and products. The Company mainly operates through three segments. The AR Advertising Services segment is mainly engaged in the provision of online holographic AR advertising solution to embed holographic AR ads into films and shows that are hosted by online streaming platforms. The AR Entertainment segment is mainly engaged in the provision of payment middleware software, game distribution platform and holographic Mixed Reality (MR) software. The Semiconductor Related Products and Services segment is mainly engaged in the provision of central processing algorithm services and computer chip products to enterprise customers and the sales of comprehensive solutions for central processing algorithms and related services with software and hardware integration.
About the author
Emily J. Thompson
Emily J. Thompson, a Chartered Financial Analyst (CFA) with 12 years in investment research, graduated with honors from the Wharton School. Specializing in industrial and technology stocks, she provides in-depth analysis for Intellectia’s earnings and market brief reports.
WiMi Hologram Cloud Unveils Quantum Hybrid Neural Network to Enhance Image Classification Efficiency
- Technological Innovation: WiMi's Lean Classical-Quantum Hybrid Neural Network (LCQHNN) framework achieves performance comparable to deep quantum circuits with only a four-layer variational quantum circuit, significantly reducing resource consumption and error accumulation risks in quantum hardware.
- Data Processing Optimization: The system utilizes lightweight convolutional layers for preliminary feature extraction, mapping high-dimensional classical features into quantum state space, forming nonlinear projections in multi-dimensional quantum Hilbert space, effectively capturing the essence of complex data distributions.
- Training Efficiency Improvement: WiMi employs an improved gradient estimation method that significantly reduces the number of quantum measurements required for each parameter update, enhancing overall training speed and stability, thus advancing quantum machine learning technology towards practical applications.
- Future Development Directions: WiMi plans to extend LCQHNN to multimodal learning scenarios, explore collaborative integration with quantum support vector machines and quantum convolutional networks, and promote the secure, efficient, and distributed construction of quantum intelligent systems.
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WiMi Unveils Quantum-Enhanced Deep Learning Technology QB-Net
- Technological Breakthrough: WiMi's QB-Net technology integrates lightweight quantum computing modules into the classical U-Net architecture, reducing bottleneck layer parameters by up to 30 times while maintaining performance comparable to classical U-Net, significantly enhancing the efficiency and application potential of deep learning models.
- Quantum Module Advantage: This technology leverages quantum states to express high-dimensional information, theoretically achieving the same mapping capabilities as traditional networks with fewer quantum bits, thereby reducing model complexity and promoting deep integration of quantum computing and deep learning.
- Structural Optimization: The design of QB-Net allows for direct embedding into existing models without altering the U-Net architecture, achieving true “plug-and-play” quantum enhancement, which improves the performance and flexibility of enterprise-level intelligent systems.
- Industry Impact: This innovation from WiMi not only demonstrates the real value of quantum computing but also provides a new structural optimization paradigm for the global AI industry, indicating that hybrid quantum-classical architectures will become a mainstream form of AI in the future.
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