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WiMi explores quantum-classical machine learning framework

Tue, 04 Aug 2026 09:12:14 -0400

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) announced it is exploring a federated training framework that combines quantum neural networks with classical pre-trained convolutional models, according to a press release from the Beijing-based company.

The proposed architecture, which WiMi calls a hybrid quantum-classical convolutional neural network (SHQCNN), uses classical convolutional models to handle feature extraction tasks while quantum neural networks built on parameterized quantum circuits handle higher-level abstract feature mapping. A federated learning layer coordinates model training across distributed nodes.

The company says the framework is designed to address two challenges in existing federated learning systems: the computational load of classical neural networks

WiMi explores quantum-classical machine learning framework

Tue, 04 Aug 2026 09:12:14 -0400

The company says the framework is designed to address two challenges in existing federated learning systems: the computational load of classical neural networks

WiMi Explores Federated Training Framework for Hybrid Quantum-Classical Machine Learning Models

Tue, 04 Aug 2026 09:10:00 -0400

BEIJING, Aug. 4, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, is exploring a federated training framework for hybrid quantum-classical machine learning models, deeply integrating quantum neural networks (QNN) with classical pre-trained convolutional models, achieving dual breakthroughs in model accuracy and training efficiency through a distributed quantum computing architecture, and opening up innovative technical paths for data privacy protection and large-scale model training.

Quantum computers, through the superposition and entanglement states of qubits, can process an exponential number

WiMi Explores Federated Training Framework for Hybrid Quantum-Classical Machine Learning Models

Tue, 04 Aug 2026 09:10:00 -0400

Quantum computers, through the superposition and entanglement states of qubits, can process an exponential number

WiMi Explores Federated Training Framework for Hybrid Quantum-Classical Machine Learning Models

Tue, 04 Aug 2026 09:10:00 -0400

Quantum computers, through the superposition and entanglement states of qubits, can process an exponential number

WiMi Explores Federated Training Framework for Hybrid Quantum-Classical Machine Learning Models

Tue, 04 Aug 2026 09:10:00 -0400

Quantum computers, through the superposition and entanglement states of qubits, can process an exponential number

WiMi Hologram Cloud researches quantum visual tracking algorithm

Mon, 27 Jul 2026 10:30:30 -0400

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) announced it has conducted research on a quantum visual tracking algorithm that combines quantum computing with computer vision technology, according to a press release from the Beijing-based company.

The algorithm uses quantum state encoding, quantum parallel computation, and quantum superposition to handle feature extraction, model training, and target detection. The company said the approach differs from traditional visual tracking algorithms, which process targets serially.

A central component of the system is a quantum-state encoded ridge regression classifier. WiMi said this classifier maps ridge regression model parameters onto quantum state amplitudes, allowing high-dimensional visual features to be

WiMi Hologram Cloud Inc. Conducts Technical Research on Quantum Visual Tracking Algorithms

Mon, 27 Jul 2026 10:30:00 -0400

BEIJING, July 27, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WIMI" or the "Company"), a globally leading technology provider, has delved into the interdisciplinary field of quantum computing and computer vision to carry out research on quantum visual tracking algorithm technology. Deeply integrating the core characteristics of quantum computing with classic visual tracking frameworks, this algorithm reconstructs the full-process logic of algorithm training and detection, achieving technological innovations at the underlying computing power and algorithm architecture levels. Boasting both theoretical novelty and engineering implementation value, it effectively resolves the

WiMi Hologram Cloud Inc. Conducts Technical Research on Quantum Visual Tracking Algorithms

Mon, 27 Jul 2026 10:30:00 -0400

WiMi Hologram Cloud Inc. Conducts Technical Research on Quantum Visual Tracking Algorithms

Mon, 27 Jul 2026 10:30:00 -0400

WiMi Hologram Cloud Inc. Conducts Technical Research on Quantum Visual Tracking Algorithms

Mon, 27 Jul 2026 10:30:00 -0400

WiMi unveils quantum neural network for image classification

Mon, 20 Jul 2026 09:02:05 -0400

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) announced the release of a quantum convolutional neural network (QCNN) for image classification, built on quantum random access memory (QRAM) architecture, according to a company statement dated July 20, 2026.

The technology targets large-scale image classification tasks and is designed to address limitations in existing QCNN models, specifically qubit count, circuit depth, and data loading efficiency. Current quantum computing devices face constraints including limited qubit availability and high noise levels, which hinder the processing of large-scale input data.

In WiMi's approach, QRAM is integrated into the feature extraction and channel mapping process of the QCNN rather

WiMi unveils quantum neural network for image classification

Mon, 20 Jul 2026 09:02:05 -0400

In WiMi's approach, QRAM is integrated into the feature extraction and channel mapping process of the QCNN rather

WiMi Releases Resource-Efficient Quantum Convolutional Neural Network Based on QRAM, Accelerating the Practical Implementation of Large-Scale Image Classification Applications

Mon, 20 Jul 2026 09:00:00 -0400

BEIJING, July 20, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the release of its latest independently developed achievement—a quantum convolutional neural network for efficient image classification based on quantum random access memory (QRAM). This technology is oriented toward typical image classification tasks with large-scale input data and multiple output channels. It systematically addresses the key bottlenecks of existing quantum convolutional neural networks in terms of the number of qubits, circuit depth, and data loading efficiency,

WiMi Releases Resource-Efficient Quantum Convolutional Neural Network Based on QRAM, Accelerating the Practical Implementation of Large-Scale Image Classification Applications

Mon, 20 Jul 2026 09:00:00 -0400

WiMi Releases Resource-Efficient Quantum Convolutional Neural Network Based on QRAM, Accelerating the Practical Implementation of Large-Scale Image Classification Applications

Mon, 20 Jul 2026 09:00:00 -0400

WiMi Releases Resource-Efficient Quantum Convolutional Neural Network Based on QRAM, Accelerating the Practical Implementation of Large-Scale Image Classification Applications

Mon, 20 Jul 2026 09:00:00 -0400

WiMi researches neural networks for quantum key distribution optimization

Mon, 29 Jun 2026 10:50:36 -0400

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) announced it is researching the use of neural networks to optimize parameters in dual-field quantum key distribution (TF-QKD) systems, according to a press release from the Beijing-based company.

The research involves training and evaluating three neural network models: a Backpropagation Neural Network (BPNN), a Radial Basis Function Neural Network (RBFNN), and a Generalized Regression Neural Network (GRNN). Each model was tested for its ability to predict optimal parameter configurations for TF-QKD systems.

WiMi said all three models demonstrated the ability to predict optimal TF-QKD parameters, with RBFNN and GRNN showing higher prediction accuracy in high-dimensional parameter

WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key Distribution