Hello. Due to the unsatisfactory development environment for quantum computers, our company is making a significant shift in its business strategy. This aligns with global trends, and many companies are implementing similar changes, which we also want to practice. Until now, quantum computers seemed to be in vogue, and a substantial amount of research and development funds were invested. However, recently, generative AI has gained more importance. In fact, an acquaintance of mine from government person told me they weren&#39;t very hopeful about quantum computing. This sentiment likely stems from the failures of quantum ventures in the US, leading to a broad understanding that investments don&#39;t yield much return. In this context, our company is focusing on a method called quantum machine learning. This method has appeared separately from quantum computers in the Japanese edition of Gartner&#39;s Hype Cycle for 2023. Currently, quantum computers are in a phase of disillusionment, and there are many questions about their practicality. While continuous research and development are essential, it is still at the foundational research stage. For businesses like ours, which focus on applied research and practical use, it feels like a distant future. In reality, practical use that takes as long as 10 or 20 years is required. Utilizing GPUs, calculations for Fault-Tolerant Quantum Computers (FTQC) with thousands of qubits have become achievable in situations with minimal quantum entanglement. Due to the computational capabilities of tensor networks with GPUs, we can perform calculations with thousands of qubits, especially for problems with limited quantum entanglement. Moreover, these methods, called FTQC, are error-free. To surpass this, actual quantum computers need to be developed with more than 50 qubits in systems with significant quantum entanglement. This means there&#39;s a need to perfectly create a machine with more than 50 qubits in flawless FTQC. Realizing such hardware within 5 or 10 years is challenging, and I believe giants like Google and IBM have postponed their practical applications due to these challenges. In the midst of this, our company heavily focuses on quantum machine learning, utilizing NVIDIA&#39;s GPUs. Calculations with low quantum entanglement can be performed even without quantum computers. By integrating this concept with GPU computations, it&#39;s integrated with neural networks, enabling us to run various applications. Hence, we&#39;ve adopted this approach. Regarding the popular annealing, while I feel that the practical application of quantum computers or quantum annealing machines is quite stringent, those camps might be attempting to accelerate calculations using the latest classical computers like GPUs. However, from our perspective, focusing on quantum machine learning, we view quantum computers as a business opportunity and have decided to advance in that direction. As for annealing or quantum annealing, our policy is to have them primarily addressed externally. Well, regarding hardware, we need to procure many GPUs. From the perspective of neural networks and quantum computing, we are procuring GPUs from NVIDIA and strengthening our cloud system to provide these to our customers. Additionally, due to an increase in more extensive projects than ever, focusing on foundational environments like networks and storage, we&#39;re aiming to dedicate maximum resources to the stabilization of our cloud environment. As for our consulting business, we plan to focus primarily on quantum machine learning when offering application consulting, basic implementation, and delivery. Quantum machine learning has a high affinity with classical machine learning. This makes it highly compatible with plans like machine learning implementation, and we believe quantum computing and machine learning can be utilized in many operational environments. We aim to adopt an approach centered on machine learning-based quantum computing, without committing to a specific domain, and instead work collaboratively with our clients&#39; representatives to improve and optimize models within their specific domains. As for the GPUs, a data center is essential. Currently, there are few AI data centers in Japan, and the know-how hasn&#39;t been accumulated, making the delivery, including general data centers, challenging for our company. Both from a cost perspective and assessment perspective, we need to significantly expand our cloud services. Therefore, we&#39;re looking to set up an environment and team for acquiring and operating GPUs in-house. We intend to continue developing semiconductor quantum computers. We feel that the emergence of semiconductor-based quantum computers will significantly change the landscape, and we want to take the lead in developing these quantum computers. Of course, our company specializes in software, so hardware is not our strong suit. However, given the current situation where Japan could significantly suffer if it falls behind in semiconductors, we want to undertake this initiative with national pride at stake. Furthermore, we aim to make significant contributions to the marketing field, especially in advertising. With the recent emergence of generative AI, the dynamics of the advertising marketing sector seem to be shifting considerably. Facing these changes, our company wishes to capitalize on new technologies, leveraging quantum computing and deep learning in this environment. At this juncture, we plan to entrust the Japan Quantum Computing Association with the introductory education in quantum computing. As for the media we&#39;ve provided so far, we&#39;re considering entrusting everything to the Quantum Computing Report&#39;s Japanese edition. Therefore, we want to focus on quantum machine learning, research and development using GPUs, and application offerings in the advertising marketing sector. Regarding research and development, we&#39;ve been relatively low-key until now. However, we have a team centered around the University of Tokyo. Moving forward, we&#39;d like our activities to be primarily based at the University of Tokyo. Concerning announcements of our achievements and presentations on quantum computing, we aim to actively participate in international conferences as a united team, covering both quantum computing and future deep learning and machine learning conferences. That&#39;s all.

blueqat Inc. is making a significant shift in its business and research direction.

Yuichiro Minato