WiMi Hologram Cloud Inc. Stock Price, News & Analysis

WiMi (NASDAQ: WIMI) announced a hybrid quantum-classical learning architecture for multi-class image classification on December 4, 2025. The design reuses measurement results from both retained and discarded qubits in quantum convolutional neural networks (QCNN), feeding them into separate classical fully connected branches that are fused and jointly trained with quantum parameters.

The approach aims to reduce information loss from quantum pooling on NISQ devices, improve expressive power of hybrid models, and enable co-evolution of quantum gate angles and classical weights via cross-entropy backpropagation. WiMi positions this as a practical path for QML under current hardware constraints, with applications in intelligent vision, medical diagnosis, and autonomous driving.

WiMi (NASDAQ: WIMI) announced a research architecture for quantum generative adversarial networks (QGANs) on Nov 20, 2025, proposing a dual-discriminator hybrid quantum-classical design built around a quantum convolutional neural network (QCNN) discriminator.

The design uses a three-layer QCNN discriminator pipeline—quantum feature encoding, parallel quantum feature extraction, and classical decision output—to improve feature capture, shorten gradient paths, and reduce gradient vanishing via particle swarm optimization of quantum gate parameters. Two QCNN discriminators are trained with dynamically balanced loss weights to force simultaneous global distribution matching and local feature authenticity, aiming to boost stability and diversity of QGAN outputs for tasks like image generation and quantum state simulation.

WiMi (NASDAQ:WIMI) announced research into a quantum generative adversarial network (QGAN) and a hybrid quantum-classical convolutional neural network for synthetic image generation and detection on November 14, 2025.

The company says the QGAN yields shorter simulation and training time and lower generator and discriminator losses versus traditional GANs, and the hybrid model pairs quantum computing with classical CNNs to improve feature extraction and classification accuracy for real vs. synthetic images.

The release frames these models as research-stage advancements that could expand applications as quantum hardware and algorithms improve.

WiMi Hologram Cloud (NASDAQ: WIMI) announced on October 24, 2025 that it is researching a blockchain privacy protection system built on post‑quantum cryptography combined with threshold key‑sharing. The design targets quantum‑resistant encryption, distributed private‑key management across consensus nodes, threshold‑based data authorization, fine‑grained access control, and support for smart contracts and scalable consensus.

The system emphasizes post‑quantum security, fault tolerance via distributed key shares, controlled data access through node thresholds, and optimizations for high concurrency and dynamic scalability.

WiMi Hologram Cloud (NASDAQ: WIMI) announced on October 24, 2025 that it is researching a blockchain privacy protection system built on post‑quantum cryptography combined with threshold key‑sharing. The design targets quantum‑resistant encryption, distributed private‑key management across consensus nodes, threshold‑based data authorization, fine‑grained access control, and support for smart contracts and scalable consensus.

The system emphasizes post‑quantum security, fault tolerance via distributed key shares, controlled data access through node thresholds, and optimizations for high concurrency and dynamic scalability.

WiMi Hologram Cloud (NASDAQ: WIMI) announced research into a shallow hybrid quantum-classical convolutional neural network (SHQCNN) for image classification on October 23, 2025.

The SHQCNN integrates an enhanced variational quantum method, kernel encoding for input mapping, variational quantum circuits in the hidden layer, and mini-batch gradient descent in the output layer to improve training speed, stability, accuracy, and generalization while avoiding high-depth QNN complexity.

WiMi (NASDAQ: WIMI) on October 20, 2025 announced development of a single-qudit quantum neural network (SQ-QNN) for multi-class/multi-task design that maps categories to dimensions of a high-dimensional qudit.

The release highlights three technical layers: quantum state encoding, unitary evolution via the Cayley transform of skew-symmetric matrices, and a hybrid quantum-classical training combining extended activation functions with SVM optimization. Claimed benefits include reduced circuit depth, lower training overhead, simplified feature steps, and improved representational efficiency for high-dimensional classification.

WiMi (NASDAQ: WIMI) announced on October 15, 2025 the development of a Quantum Semi-Supervised Learning (QSSL) framework that uses claimed quantum supremacy to address limited labeled data and computational bottlenecks in machine learning.

The release describes three core components: a quantum matrix product estimation algorithm, a quantum self-training Propagation Nearest Neighbor classifier, and a quantum semi-supervised K-means clustering algorithm. WiMi says the quantum-classical hybrid approach shortens training time, enables larger dataset processing via parallelism, and aims to improve classification and clustering accuracy. No financial metrics, commercialization timeline, or binding contracts were disclosed.

WiMi (NASDAQ: WIMI) announced on October 15, 2025 the development of a Quantum Semi-Supervised Learning (QSSL) framework that uses claimed quantum supremacy to address limited labeled data and computational bottlenecks in machine learning.

The release describes three core components: a quantum matrix product estimation algorithm, a quantum self-training Propagation Nearest Neighbor classifier, and a quantum semi-supervised K-means clustering algorithm. WiMi says the quantum-classical hybrid approach shortens training time, enables larger dataset processing via parallelism, and aims to improve classification and clustering accuracy. No financial metrics, commercialization timeline, or binding contracts were disclosed.