Research in Intersection of Quantum Computing And Databases

Welcome to my academic webpage! Since the biography in the beginning is good to keep short, I explain my academic interests and motivate my research deeper in this post. I started as a Ph.D. student in April 2022 at the University of Helsinki in the computer science department in the Unified Database Management Systems research group. Before my Ph.D. studies, I worked as a research assistant in the same group.

If you check my previous publications, I have not been researching quantum computing but applications of category theory for multi-model databases. Usually, a relatively small science community can understand your research specifically. In my case, I felt that the community was even smaller than usual. Understandably, the database research community is not familiar with applied category theory. On the other hand, the results were not very deep from a category theory point of view. Although applied category theory is exciting and topical, I noticed that category theory and databases are relatively narrow research topics.

I have studied special linear algebraic topics in my thesis, e.g., quadratic forms and Pfister forms. After all, quadratic and Pfister forms are matrices, and their manipulation requires advanced algebraic results. Because quantum computing is theoretically based on linear algebraic structures, I can relatively easily apply my previous knowledge and adopt quantum computing in my research. I have not officially studied quantum computing because our university had only a single quantum computing course, and I did not find it in my student times. Our department has plans to extend quantum computing teaching, which is a very positive direction. I believe that I will surely be a part of the development of education.

I do not believe that quantum computing will bring immersive computational speedups in the near future. In terms of speed, the promises of quantum computing are sometimes over-hyped. When I represent my research, I aim to be realistic regarding its possibilities. On the other hand, time is just a single ‘‘parameter’’ that we want to minimize. Sometimes we should move our focus away from time efficiency. Whereas we can keep arguing if quantum computing can perform faster than classical, we can be sure that quantum computing is much more energy efficient than classical. We have read news about bitcoin mining which consumes a tremendous amount of energy, and the data centers require their own nuclear powerplants before their energy consumption is covered. Energy will be literary a disappearing resource in the world. Also, we are currently living in the very last moments when we can still do something against the climate crisis. Here, quantum computers can save a part of the field because their energy consumption is at the level of your fridge at home. Sometimes you might want to trade speed with energy.

The second motivation to add quantum computing along with databases and category theory is the conceptual model. Quantum computing is a fundamentally different computing model. Learning to think quantumly is a fascinating (and very long) journey! Quantum computing is intriguing, even if we never find any serious applications for it.

How can we apply quantum computing in data management? That is my research’s core question, and the possible ideas deserve their own post. I have written a detailed Ph.D. workshop paper for VLDB 2022 conference. If it gets accepted, I will link it here. Anyway, I want to point out that I want to continue researching the possibilities of category theory in the context of quantum computing and databases. There is already extensive research connecting category theory and quantum computing. The pioneer of the field is Bob Coecke. Research linking quantum computing and databases is still in its initial steps, and as far as I know, the database community is not aware of its possibilities.