Re-evaluating Tensor Networks: The Fusion of Quantum Computing and Deep Learning
Recently, I have not consciously focused on it, but there is a technology within our field that remains difficult to understand yet continues to prove its value. That technology is the fusion of quantum computing and deep learning through tensor networks.
Tensor networks have often been criticized as "too difficult," "useless," or "not truly quantum computing." However, upon reflection, I have reaffirmed that the reason we have been able to efficiently achieve both deep learning and quantum computing within our company is precisely due to tensor networks.
Why Are Tensor Networks Important?
The greatest strengths of tensor networks can be summarized in the following three points:
1. Efficient Computation Using Multi-GPU Systems
Both deep learning and quantum computing require immense computational resources. However, by leveraging tensor networks, large-scale computations can be efficiently executed using multi-GPU systems, dramatically improving computational performance. Additionally, the evaluation of quantum entanglement within tensor networks is directly linked to the optimization of deep learning computations. DeepSeek, a deep learning framework that has been making headlines, also focuses on improving computational efficiency. The demand for low-resource compression techniques in information processing remains strong.
2. Broad Technical Learning and Efficient Engineer Training
Quantum computing and deep learning are often seen as separate fields. However, by utilizing tensor networks, engineers can learn both technologies simultaneously. This enables more efficient training of engineers while also expanding their areas of expertise.
3. Future Applications to Quantum Computers
The techniques developed through tensor networks can be directly applied to future quantum computers. Not only do they enhance the current hybrid quantum computing environment, but they also pave a clear roadmap for transitioning to fully quantum computing systems in the future. This clarity in the long-term vision is now proving to be invaluable.
Future Outlook
The practical potential of tensor networks, which has often been overlooked, is now becoming increasingly evident. As the integration of quantum computing and deep learning progresses, tensor networks will undoubtedly become even more crucial.
By focusing on this technology, we aim to drive further advancements in both quantum computing and AI, exploring practical applications while continuing our research and development efforts.