Young Researcher Spotlight: Matias Karjula on Quantum Computing and HPC

1. Your recent work touches on quantum computing and quantum reservoir computing - what research problem are you most focused on right now, and what makes it especially interesting from your perspective?

Recently I’ve spent a lot of time on the question of computationally useful structure of quantum subsystems. Certain types of quantum systems generate useful subsystem structure for information processing, while others do not, and understanding the reasons behind this difference is especially interesting to me.

I am also interested in developing new quantum algorithms and testing them on quantum hardware. Even if current devices are still limited in scale, it feels very special to build an algorithm connected to a real-world problem and run it on an actual quantum computer. It really feels like we are at the beginning of a new era.

2. Quantum systems can be mathematically and conceptually demanding. Which modelling techniques, simulations, or computational tools have become most important in your day-to-day research work?

When doing research related to quantum computing and algoritms, its often very important to know how things scale and this might not be obvious at all from the analytics. Estimating the scaling numerically is very recource extensive thus the need for HPC appears very quickly when I try estimate something numerically at a larger scale.

Access to real quantum computers has also become increasingly important. Simulations can take you far and teach a great deal about an algorithm in theory, but the hardware reality is often very different. Working with actual quantum devices has forced me to think more practically about noise, limitations, and implementation details. In Aalto University we are very lucky because we have both our own quantum computer and HPC resources dedicated for research.

AI also has become an important everyday tool for me, especially for coding, literature work, and clarifying ideas.

3. Aalto University has a strong interdisciplinary research environment: how have discussions with physicists, computer scientists, or collaborators from other fields influenced the way you approach your own research questions?

Discussions with physicists both inside and outside my own group have been very important for me. They have helped me clarify what the important questions are and how to approach them.

I have been especially fortunate to work with Dr. Moein Ivaki, who has had a major positive influence on how I do research. I am also very thankful to Professor Tapio Ala-Nissilä for many interesting discussions and for the opportunities he has given me.

Discussions with researchers from other fields are also valuable because they often bring different ways of thinking. They can disrupt your usual perspective in a positive way and force you to look at the bigger picture outside your own bubble.

4. In emerging fields like quantum technologies, uncertainty is part of the process. Can you describe a moment when an experiment, simulation, or theoretical result challenged your assumptions and forced you to rethink your approach?

This has happened many times. I have often thought that I understood something, become excited about it, told everybody, and then later realized that I was wrong or that the picture was incomplete. It has taught me to be more critical of my assumptions and conclusions.

On the positive side, I have usually learned something important from being wrong. I am also fortunate to have very supportive colleagues and collaborators who tolerate my random process.

5. Quantum computing is often discussed in futuristic terms. If you had to explain to a non-specialist where your research could realistically have an impact over the next decade, what examples would you give?

For quantum reservoir computing, I think the most realistic impact is in problems involving complex time-dependent data, perhaps from a quantum process. The idea is to use the natural dynamics of a quantum system as an information-processing resource, and then train a classical readout to make predictions.

Over the next decade, QRC could have an impact for example in nonlinear time-series processing, classification of physical data, or quantum sensing. I would not present it as a replacement for classical machine learning, but as a possible hybrid approach for very specific types of problems.

6. As an early-career researcher working in a rapidly evolving field, what skills or mindsets have proven most valuable so far, and what advice would you give to students hoping to enter quantum technology research today?

I am still early in my career, so I can mostly say what has helped me so far. One thing is trying to work as hard as possible, while also trying to keep the rest of life in balance. Research can easily take over everything for me, so it has been very important to have things in life that are not related to science, such as sports and spending time with my wife. In research I have also found it useful to stay open-minded to new ideas, to try things out with a very low threshold, and to be somewhat systematic in what I do.

For students entering quantum technology research, I would suggest trying to find questions that are genuinely interesting to them and meaningful at the same time. At least for me, those questions have become clearer by trying different things, listening carefully to more experienced researchers, and reading. I do not do the last one nearly enough, but I think its important.