The architecture operates as a hybrid quantum-classical system, mapping classical data into quantum states using block partitioning for images and structured encoding for one-dimensional datasets. Within the feature extraction module, the network alternates between traditional quantum convolution and the newly developed interaction layers. These layers facilitate cross-channel information fusion, allowing the model to capture nonlinear correlations without the need for excessively deep circuits that often struggle with hardware noise.

Theoretical analysis conducted by the WiMi R&D team indicates that these three-body interactions significantly expand the reachable state space, effectively bypassing expressivity limitations found in earlier quantum models. By generating high-intensity entanglement at shallower depths, the system maintains stable performance across both binary and multi-class classification, even when subjected to noisy environments. To address training instability, the company implemented a joint iterative optimization strategy that refines parameter initialization to prevent gradient vanishing.

Moving forward, the company intends to scale this technology for high-dimensional image analysis, time series forecasting, and cross-modal data fusion. The objective is to transition quantum algorithms away from mere acceleration tools toward native intelligent systems capable of operating on real-world quantum hardware. This development signals a shift in quantum deep learning, moving from mimicking classical neural structures to fully exploiting the intrinsic multi-body mechanics of quantum physics.