MicroCloud Hologram Inc. Advances Digital Simulated Quantum Computing with DeepSeek Model

MicroCloud Hologram Inc., a technology service provider, has announced a significant advancement in digital simulated quantum computing through the introduction of the DeepSeek model. This development enhances the efficiency of quantum computing simulations and offers new insights for designing and optimizing future quantum algorithms.

Digital simulated quantum computing is crucial for researching quantum algorithms, especially given the current challenges in hardware implementation, such as qubit stability and error rate control. The DeepSeek model optimizes the simulation and prediction of complex quantum systems, significantly reducing the computational resources required.

The DeepSeek model employs a new neural network architecture called the Quantum Tensor Network Neural Network (QTNNN), which consists of multiple layers of quantum tensor nodes. This architecture, combined with deep learning technology, allows for automatic learning of tensor network structures and parameters, reducing computational resource consumption by over 50% and improving simulation accuracy by more than 30%.

This breakthrough not only addresses the limitations of traditional digital simulation methods but also accelerates research in fields like quantum chemistry and quantum machine learning. As quantum computing hardware continues to develop, the optimized digital simulation technology will provide strong theoretical support and algorithmic reserves, facilitating the transition of quantum computing from research to industrial applications.