#China #Beijing #Quantum Neural Network #WiMi Hologram Cloud #Image Classification

WiMi Hologram Cloud Inc. Launches Advanced Hybrid Quantum Neural Network

WiMi Hologram Cloud Inc. (NASDAQ: WiMi), a frontrunner in Hologram Augmented Reality (AR) technology, has unveiled its latest innovation: a hybrid quantum neural network structure designed to revolutionize image multi-classification. This cutting-edge technology marries the spatial feature extraction strengths of classical convolutional neural networks (CNN) with the advanced capabilities of quantum neural networks (QNN). The result? A new hybrid architecture showcasing superior generalization and computational efficiency in multi-class classification scenarios, poised to set a new standard in image processing.

The Hybrid Structure and Its Components

The novel hybrid quantum neural network (H-QNN) integrates three primary modules: the feature dimensionality reduction and encoding module, the quantum state transformation module, and the hybrid decision and transfer learning module. Each plays a crucial role in enhancing classification accuracy and alleviating common issues found in traditional models.

1. Feature Dimensionality Reduction and Encoding: This initial stage employs a classical CNN structure, extracting low-dimensional image features through convolutional and pooling layers. The resulting feature vectors undergo PCA dimensionality reduction and are prepared for quantum encoding via an improved angle encoding technique. This mapping transforms real-valued features into quantum state amplitudes, ensuring minimal noise and reducing quantum gate depth.

2. Quantum State Transformation: At the core of the H-QNN are several layers of quantum circuits designed for nonlinear discrimination. These layers, comprising parameterized rotation gates and controlled entanglement gates, facilitate high-dimensional feature mapping while effectively preventing gradient vanishing—a common pitfall in deep learning models. This innovative design allows the model to navigate the complexities of multi-class tasks with stable convergence.

3. Hybrid Decision and Transfer Learning: The final module integrates quantum computing results with classical decision-making frameworks. By converting quantum measurement probabilities into feature vectors, this hybrid approach fuses the strengths of both neural network types. Incorporating a transfer learning mechanism enables the model to quickly adapt to new tasks, significantly reducing training epochs while enhancing performance stability.

Implementation and Technical Innovations

The H-QNN structure is adept at functioning in both simulation environments and on hardware quantum processing units (QPU). While high-performance GPU clusters manage classical module training, quantum modules operate in simulators or FPGA-accelerated quantum environments. This heterogeneous computing model exemplifies WiMi's commitment to pushing the boundaries of current quantum technology limits.

Key innovations within this hybrid architecture include:

• - A comprehensive integration of CNN and QNN, moving beyond traditional models that merely utilize quantum elements in classification heads.
• - An advanced encoding strategy that mitigates quantum encoding limitations, thereby maximizing quantum information utility.
• - A unique combination of transfer learning and parameter sharing strategies that effectively addresses gradient vanishing and overfitting risks, facilitating quicker convergence in multi-class environments.
• - An efficient use of FPGA modules for quantum computations, allowing for nanosecond-level state updates, significantly enhancing the overall training speed of the system.

The Future of Quantum Intelligence

With the launch of this hybrid quantum neural network, WiMi Hologram Cloud Inc. signals a pivotal advancement in the field of quantum artificial intelligence, bridging theoretical exploration with practical applications. This technology not only leverages the benefits of quantum computing in machine learning but also mitigates current hardware performance challenges. By embedding quantum layers within classical neural networks, WiMi is setting the stage for quantum intelligence to incorporate seamlessly into various domains, from deep learning to edge computing. As this technology evolves, it promises to significantly impact the development trajectory of an intelligent society, moving quantum advancements from the confines of laboratories to real-world applications, enhancing industrial processes and augmenting human cognitive capabilities.