HOLO’s DeepSeek model introduces the Quantum Tensor Network Neural Network.
SHENZHEN, China, Feb 24, 2025 – MicroCloud Hologram Inc. announced that the introduction of the DeepSeek model has improved digital simulated quantum computing. This breakthrough not only enhances the simulation efficiency of quantum computing but also provides new insights for the design and optimization of future quantum algorithms. Especially in the context where hardware implementation is not yet mature, digital simulated quantum computing has become an important tool for researching and developing quantum algorithms.
Quantum computing utilizes the superposition and entanglement properties of quantum bits (qubits) to achieve exponential speedup in computation for certain specific problems. However, the hardware implementation of quantum computers still faces numerous technical challenges, such as qubit stability and error rate control. As a result, digital simulated quantum computing has become an important tool for researching and developing quantum algorithms.
Digital simulated quantum computing uses classical computers to simulate the behavior of quantum systems, helping researchers understand and design quantum algorithms. However, as the scale of quantum systems increases, the computational resources required for simulation grow exponentially, making it extremely difficult to simulate large-scale quantum systems. HOLO, through the DeepSeek model, focuses on optimizing the simulation and prediction of complex systems. Its powerful computational optimization capabilities and flexible architecture make it an ideal tool for optimizing digital simulated quantum computing.
The state of a quantum system can be described by a wave function, which is a complex vector that exists in Hilbert space. For a system containing n qubits, the size of its wave function is 2^n, which makes directly simulating large-scale quantum systems extremely difficult.
To reduce the computational resources required for simulating quantum systems, the Tensor Network method has been introduced. Tensor networks effectively reduce computational complexity by decomposing high-dimensional tensors into products of lower-dimensional tensors. However, traditional tensor network methods still face challenges when dealing with large-scale quantum systems. HOLO, using the DeepSeek model and deep learning technology, has optimized the construction and updating process of tensor networks. By leveraging neural networks in the DeepSeek model to automatically learn the structure and parameters of the tensor network, it significantly reduces the consumption of computational resources while ensuring simulation accuracy.
HOLO, through the DeepSeek model, has developed a new type of neural network architecture called the “Quantum Tensor Network Neural Network” (QTNNN). QTNNN consists of multiple layers, each containing several quantum tensor nodes. These nodes are interconnected in a specific manner to form a complex network structure.
The training process of the DeepSeek model is divided into two stages: pre-training and fine-tuning. In the pre-training phase, the model is trained using a large amount of quantum system data to learn the basic structure and parameters of the tensor network. In the fine-tuning phase, the model is optimized for specific quantum systems, further improving the simulation’s accuracy and efficiency.
By introducing the DeepSeek model, HOLO has optimized the algorithms for digital simulated quantum computing. The optimized algorithm significantly reduces the computational resources required. Through the automatic learning of tensor network structures and parameters, the computational resources needed for simulating quantum systems are greatly reduced. Experiments show that the optimized algorithm reduces the consumption of computational resources by more than 50% when handling large-scale quantum systems.
In addition, the accuracy of digital simulation for quantum computing has been significantly improved through optimization. HOLO, utilizing the DeepSeek model’s deep learning technology, is able to capture the behavior of quantum systems more accurately. Experiments show that the optimized algorithm has enhanced simulation precision by over 30%, especially when handling complex quantum systems, where its performance is particularly remarkable.
The breakthrough achieved by HOLO through the introduction of the DeepSeek model in the field of digital simulation quantum computing marks a significant step in the deep integration of quantum computing research and deep learning technology. This breakthrough not only addresses the bottleneck issues of traditional digital simulation methods in terms of computational resources and efficiency but also provides entirely new tools and ideas for the design and optimization of quantum algorithms. With the implementation of this technology, researchers are able to simulate large-scale quantum systems more efficiently, thereby accelerating research in fields such as quantum chemistry, quantum machine learning, and quantum optimization algorithms. The successful application of this technology not only demonstrates the enormous potential of deep learning in scientific computing but also lays a solid foundation for the practical applications of future quantum computing. As quantum computing hardware continues to mature, the optimization of digital simulation technology will provide strong theoretical support and algorithmic reserves, propelling quantum computing from the laboratory into industrial applications.
From a technical implementation perspective, HOLO utilizes the DeepSeek model and its unique Quantum Tensor Network Neural Network (QTNNN) architecture to successfully integrate deep learning with quantum system simulation. This architecture not only automatically learns the complex structure and dynamic behavior of quantum systems but also significantly reduces computational resource consumption while maintaining simulation accuracy. Experimental results show that the optimized algorithm reduced computational resource consumption by over 50% and improved simulation accuracy by more than 30% when handling large-scale quantum systems. This dual enhancement in efficiency and accuracy allows researchers to verify and optimize quantum algorithms more quickly, thereby accelerating the practical application of quantum computing technology.
The technological breakthrough achieved by HOLO is not only of significant importance in the field of quantum computing but will also have a profound impact on scientific research and industrial applications. In scientific research, the optimized digital simulation technology will provide more powerful tools for fields such as quantum chemistry, materials science, and drug development, helping scientists gain a deeper understanding of the behavior of complex quantum systems. In industrial applications, the accelerated development of quantum computing will bring new opportunities to industries like finance, energy, and artificial intelligence, such as more efficient financial modeling, more precise energy optimization algorithms, and more powerful machine learning models. This will promote global scientific collaboration and innovation, accelerating the widespread adoption and application of the technology. It is foreseeable that as quantum computing technology continues to mature and optimize, human society will usher in a technological revolution driven by quantum computing.
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