WiMi builds quantum network for better data sorting
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) is applying a quantum convolutional neural network, built with three-qubit interactions, to classify classical data. The company’s new network aims to improve expressive power and entanglement generation, key to advancing quantum deep learning model design. WiMi’s approach utilizes block partitioning for image data and combines structured amplitude and angle encoding for one-dimensional data, efficiently translating classical information into a quantum format. Three-Qubit Interactions Enhance Quantum Convolutional Neural Network Expressivity WiMi Hologram Cloud Inc. This approach deviates from the typical focus of quantum computing on problems intractable for conventional computers, instead targeting established machine learning tasks with a novel quantum architecture. A core innovation lies within the network’s interaction layers, specifically designed around three-qubit interactions. WiMi researchers systematically studied how these layers impact the quantum state space coverage, finding that introducing three-body interactions significantly expands the range of reachable states within the network’s parameter space. This expansion directly addresses a common limitation in traditional quantum neural networks, a lack of expressive capacity for complex patterns, while simultaneously maintaining manageable circuit depth. The team further analyzed the network’s entanglement capabilities through quantum information theory, revealing that the three-qubit interaction layer generates high-intensity, multi-scale entanglement at relatively shallow depths, which is crucial for capturing nonlinear correlations within input data and offers a clear advantage over models relying solely on two-qubit entanglement gates. For image data, WiMi employs block partitioning and local mapping to embed pixel information into quantum subsystems, while one-dimensional data benefits from a combination of structured amplitude encoding and angle encoding,
