Hello, AI is booming, isn't it? Recently, solutions utilizing GPUs have been growing rapidly in our company. When trying to deal with models that hold vast datasets or intend to perform specific calculations, there's inevitably a need to use high-performance computing, especially during a proof of concept (PoC).
What we highly recommend at our company is to learn about quantum computing while also studying machine learning and deep learning, and then even advancing it into a business venture. What we particularly recommend is the use of GPUs. By using GPUs, there are a full set of tools available, and computation time can be significantly reduced. Therefore, even if the final implementation is on a CPU or on a machine that doesn't deliver high performance, by initially trialing with a GPU, you can verify the direction to take.
We can prepare GPUs on our end, or we can integrate the GPU tools you use with ours and collaborate. Typically, we recommend using our cloud service, where you can use the GPUs installed, and transfer and verify files. Especially regarding quantum computing, the methods are somewhat new, so we often deal with problems that aren't widely documented. Combining it with machine learning requires a lot of time for verification and communication. To efficiently use this time, our cloud service is frequently used as a communication tool for exchanging reports and files."
When trying to use quantum computing on its own, it's still quite a ways from practical application. Therefore, the primary uses are often for educational purposes or as trials. However, the services our company offers adopt an approach where you can either use a quantum computer or a GPU. Consequently, it is now possible to consider both practical computations using GPUs and potential future applications on quantum computers simultaneously.
The main computations available are centered on deep learning. Deep learning primarily has an internal structure known as neural networks. Our service aims to lighten and speed up such neural networks and general matrix computations using quantum entanglement.
So, for initial trials, you can use standard deep learning models and datasets to evaluate accuracy and performance. Generally, the goal is to construct a more lightweight and efficient neural network model, especially for models with many parameters. Therefore, the business application is quite straightforward. If you are interested, please contact our sales representatives. You can get in touch through our inquiry section. That's all.