Wimi Hologram Cloud - Class B Share Price Today

WiMi develops quantum circuit optimization architecture using reinforcement learning

WiMi Hologram Cloud Inc. unveiled a reinforcement learning-based quantum encoding circuit optimization architecture. The solution uses model-based RL to predict circuit performance, reducing hardware evaluation needs. Hierarchical circuit decomposition lowers search dimensions and improves efficiency. The system optimizes for model performance, resource consumption, and noise robustness simultaneously.

WiMi Hologram Cloud unveils quantum neural network for data classification

WiMi introduces a quantum convolutional neural network using three-qubit interactions for classical data classification. The hybrid architecture maps classical data to quantum states via block partitioning or amplitude encoding. Three-body interactions expand parameter space reachability while keeping circuit depth shallow. Training uses joint iteration between classical optimizers and quantum parameters for stable convergence. WiMi plans to scale the model for higher-dimensional images and time series analysis.

WiMi Hologram Cloud releases hybrid quantum neural network for image recognition

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) unveiled Hybrid Quantum Neural Network (H-QNN) technology for image recognition. The system combines quantum circuits with classical networks, achieving improved accuracy and efficiency on the MNIST dataset. Future plans include expanding to complex datasets and industrial applications like autonomous driving and financial analytics.

WiMi Hologram Cloud explores hybrid quantum-classical machine learning framework

WiMi Hologram Cloud Inc. is exploring a federated training framework for hybrid quantum-classical machine learning models. The company claims this integration improves accuracy and efficiency while enhancing data privacy. The framework uses classical models for basic feature extraction and quantum neural networks for complex nonlinear mapping. No financial metrics or performance data were disclosed in the announcement.

MicroCloud Hologram launches DSQ-Net for noisy image classification

MicroCloud Hologram Inc. introduced DSQ-Net, a hybrid quantum-classical neural network for noisy image classification. By embedding Variational Quantum Circuits into Spiking Neural Networks, the system achieves over 90% accuracy on noisy test images. The technology offers improved robustness against noise interference compared to classical models, validated via high-fidelity simulation.

MicroCloud Hologram advances quantum algorithm technology

MicroCloud Hologram Inc. has developed a proprietary technology for approximate quantum state preparation and an entanglement-dependent complexity algorithm. The technology reduces circuit depth by over 50% and improves performance on noisy intermediate-scale quantum devices. It offers potential applications in quantum machine learning, financial risk evaluation, and material simulation.