WiMi Hologram Cloud announced the release of a Hybrid Quantum-Classical Neural Network technology for efficient MNIST binary image classification. This breakthrough achievement marks a new progress in quantum machine learning moving from theoretical exploration toward practicalization, and also embodies the enterprise's core competitiveness in the field of quantum intelligent algorithm research. This technology takes an efficient hybrid structure, scalable quantum feature mapping mechanism, and quantum state optimization strategy as its core, successfully achieving excellent classification performance on the MNIST handwritten digit dataset, proving the practical feasibility and computational advantages of quantum neural networks in high-dimensional image recognition tasks.

10/23 08:15

WiMi Hologram Cloud reveals plans to investigate SHQCNN model

WiMi Hologram Cloud announced that they are actively exploring a shallow hybrid quantum-classical convolutional neural network, SHQCNN, model, bringing innovative breakthroughs to the field of image classification. "WiMi has adopted an enhanced variational quantum method in the SHQCNN model, laying a solid foundation for the model's efficient operation in image classification tasks. The enhanced variational quantum method has undergone multi-faceted optimizations based on traditional methods," the analyst tells investors.

10/20 08:14

WiMi Hologram Cloud Advances Technology for Single-Qubit Quantum Neural Networks

WiMi Hologram Cloud announced the development of single-qubit quantum neural network technology for multi-task design. This technology has extremely disruptive significance; this technology, by demonstrating the feasibility of high-dimensional quantum systems in efficient learning, provides a realistic path for the deep integration of future quantum computing and artificial intelligence.

8/7 10:05

WiMi Hologram Cloud reports cash, cash equivalents reach $455M

WiMi Hologram Cloud announced a significant improvement in its operating performance, with a notable increase in cash reserves. As of August 7, 2025, the Company's total cash, cash equivalents, and short-term investments - including total cash and Bitcoin-related securities derivatives and short-term investments - reached approximately RMB 3.266B. Among these, bank cash alone amounted to approximately RMB 1.741B, while Bitcoin-related securities derivatives and short-term investments totaled approximately RMB 1.525B. The Company plans to allocate USD 450 million in investments toward quantum technology and holographic technology applications, while increasing capital deployment in blockchain and Bitcoin-related cryptocurrency applications. WiMi's financial model in emerging technology sectors has shown solid performance, with cash reserves continuing to grow. This milestone financial achievement clearly demonstrates the successful execution of the Company's efficient operational strategies and the strength of its management capabilities.

4/23 08:10

WiMi Hologram Cloud develops QFNN algorithm

WiMi Hologram Cloud announced the development of a Quantum Computing-Based Feedforward Neural Network algorithm aimed at overcoming computational bottlenecks in traditional neural network training. The core innovation of this algorithm lies in efficiently approximating the inner product between vectors while utilizing Quantum Random Access Memory to store intermediate computational values, enabling rapid retrieval. WiMi's QFNN training algorithm relies on several key quantum computing subroutines, with the most critical components being the quantized feedforward and backpropagation processes. In classical neural networks, feedforward propagation is used to compute the activation values of input data, while backpropagation adjusts weights to minimize the loss function. WiMi's quantum algorithm provides exponential speedup in both stages, enabling neural networks to achieve convergence in significantly less time. Quantum Feedforward Propagation: Classical feedforward propagation involves multiple matrix-vector multiplications. WiMi's quantum algorithm leverages quantum state superposition and coherence to perform these operations. Specifically, it encodes neuron weights and input data in quantum coherent states and completes matrix-vector operations through the evolution of quantum states. This approach can perform computations in logarithmic time, greatly reducing the computational load. Quantum Backpropagation: In neural network training, error backpropagation is the most critical component. The BP algorithm involves computing the gradient of the loss function and propagating it back to earlier layers of the network to update weights. WiMi's quantum algorithm leverages quantum coherent states to compute gradients and accelerates gradient calculations using the Quantum Fourier Transform, enabling gradient updates that are quadratically faster than traditional methods. Quantum Random Access Memory: In classical neural network training, each weight update requires accessing and storing a large number of intermediate computation results. QRAM allows these intermediate results to be stored in quantum states and retrieved efficiently for subsequent calculations. The advantage of QRAM lies in its ability to avoid redundant computations and provide exponential speedup.

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