Key challenges in traditional systems

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) recently announced that it is exploring an innovative hybrid quantum-classical machine learning federated training framework designed to address core challenges in traditional federated learning frameworks, specifically regarding data privacy protection and training efficiency. Federated learning employs a distributed training mechanism where "data remains stationary while models move," allowing local devices to retain raw data and only share model parameters with a central server for aggregation. This effectively mitigates the risk of data leakage associated with centralized data storage. However, the classical federated learning framework still faces two major hurdles: the computational overhead of classical neural networks on edge devices leads to lower training efficiency, and the communication costs of transmitting model parameters rise sharply as the number of devices increases. The integration of quantum computing with federated learning offers a solution to these problems. The parallel computing power of quantum algorithms can reduce the complexity of optimization problems in high-dimensional feature spaces from exponential to polynomial levels, while the distributed architecture of federated learning provides a natural vehicle for the engineering deployment of quantum models. WiMi has built a deep collaborative hybrid architecture that uses a classic pre-trained convolutional model for low-level feature extraction tasks, leveraging its proven strengths in learning basic features like image textures and edges. A quantum neural network, composed of parameterized quantum circuits, handles the non-linear mapping of high-level abstract features, enhancing feature expression capabilities through quantum entanglement properties. Ultimately, the federated learning framework enables collaborative optimization of models across multiple devices.

Three major breakthroughs in technical implementation

This innovative architecture achieves three major breakthroughs in technical implementation. At the model design level, it adopts a hybrid quantum-classical convolutional neural network structure. The input layer uses a kernel encoding method to map image data from a low-dimensional space to a high-dimensional quantum feature space, solving the adaptation problem between quantum states and classical data. The hidden layer introduces an enhanced variational quantum circuit, which requires only a few dozen qubits to achieve feature extraction capabilities comparable to classic deep convolutional neural networks, while avoiding the accumulation of quantum noise caused by increased circuit depth. In terms of the federated training mechanism, WiMi has designed a hierarchical aggregation communication protocol. Local devices only retain the classic pre-trained convolutional base model and a lightweight quantum processor. After completing local data feature encoding and model training through the quantum circuit, they only upload encrypted quantum parameter gradients to the central server. The central device uses quantum state technology to complete cross-device parameter aggregation, then generates global update parameters via a classic optimizer and distributes them to each device. As quantum hardware matures and algorithms are continuously optimized, this hybrid quantum-classical federated learning framework is expected to provide technical support for the transformation of next-generation artificial intelligence infrastructure.