Her master's thesis brought her to the Fraunhofer Institute for Cognitive Systems IKS, where she worked on a project in the field of quantum optimization. Essentially, the aim was to find quantum algorithms that solve classical optimization problems. “There is a popular candidate among the algorithms that has gained a lot of momentum over the last ten years, but not so much in the last three years,” Adelina begins her explanation. She is referring to Variational Quantum Algorithms (VQAs), a class of hybrid quantum algorithms in which the problem is represented on a quantum computer but optimized classically. “In my master's thesis, the original plan was for me to analyze such an algorithm and, in the best case, show how great it is,” says the doctoral student. However, not least because of her critical second supervisor – Frank Pollmann, professor at the Chair of Theoretical Solid State Physics at the Technical University of Munich – she shifted her focus during her work to analyzing what happens when the noise that is always present in quantum computing is taken into account and, at the same time, the problem sizes are scaled higher and higher. Adelina came to a very clear conclusion: “Basically, I showed that the larger the system becomes, the less robust the algorithms are to this noise, and therefore no solution can actually be found for large problems.” What sounds less than encouraging at first glance was an exciting starting point for Adelina's doctoral thesis: “It was such a cool result in the sense that it was so clear and negative that I wanted to build on it,” she says. She wants to find out how far these problems extend, including for other algorithms, in order to then conduct research in a positive direction – which algorithms do not have this problem, what scales? In algorithmics, very small problems are often solved and then people are happy that the solution works on a quantum computer, says Adelina. “But the fundamental question is whether quantum computing works for large systems, where it actually gives us a quantum advantage.” This is exactly what she is researching in her doctoral thesis: Are there quantum algorithms that really benefit us?

Countering hype with knowledge

For her master's thesis, Adelina was awarded first place in the master's category of the Quantum Future Award 2025 by the German Federal Ministry of Research, Technology, and Space (BMFTR). “I was very happy and proud when I received this award,” she says. The thesis has since been published in the journal “APS Physical Review A.” Now, her doctoral thesis is about completing what she started in her master's thesis by describing her numerical results theoretically. “And I'm currently working on methods to repeat my experiments for much larger-scale systems,” Adelina explains. Specifically, she is working on a simulator that should function efficiently for larger systems. “Simulator is the keyword,” explains the scientist, “because I'm not currently running my tests on real quantum computers.” She is interested in the scenario in which a quantum computer works really well – she considers the current hardware to be too error-prone for this: “I want to look at the error-free results. The only way to do that is by simulating these quantum systems.” But of course, this also has its limits, as these systems grow exponentially. Specifically, she is looking for promising methods, trying to implement and simulate them, and then evaluating them.

Adelina is no longer just looking at the hybrid VQAs that started it all, but is also investigating pure quantum algorithms – a completely different approach. With a hybrid algorithm, the problem is only represented on a quantum computer; optimization always takes place classically, in an iterative process. This is an approach in which errors can easily accumulate, as the inaccuracies in the quantum computer's results are multiplied over and over again. If, on the other hand, optimization were also performed directly on the quantum computer, everything would happen on one machine and ultimately be measured once for the solution. “Intuitively, this makes more sense, and it remains to be seen whether this will translate into practice,” says Adelina, commenting on this method.

“While others are working on developing quantum computers, I am looking at what we can do with them and analyzing the algorithms that are supposed to run on them,” says Adelina, summarizing her research. “In reality, we only have a handful of algorithms that achieve a quantum advantage for very specific problems, and we have to use them cleverly to solve a general problem from the real, the classical world with an advantage,” she continues. Finding something like that is not so easy. But even the realization that something does not work sensibly is a significant result for her. It is important to the MQV fellow to remain critical and counteract the hype that the field of quantum computing often experiences. “Not in a negative sense, but with knowledge,” as she says.

However, the young scientist is certainly optimistic about the application of quantum computers in certain areas. And she has no shortage of motivation to get down to work on solving difficult questions along the way. “In the past, I was motivated by not understanding things, and that's still the case today; it really drives me,” she says. But what she finds truly fulfilling are the moments of success when she has thoroughly understood a problem or a concept to such an extent that she has been able to implement it successfully and can actually see in her numerical results what the theory predicted. Then quantum mechanics loses any mystical aura that is sometimes attributed to it and that Adelina finds difficult to relate to: “Actually, you can simulate all of that. Or you could simulate everything if you had infinite computing resources. It's not all that unpredictable.”

Achieving something good

Of course, sometimes things don't go so smoothly in her work – “that happens often, very often!” – but what always helps her then is to look beyond her own field of research, says Adelina, whether it's reading a paper from another field or talking to other people at a conference. The latter in particular is something the scientist loves about her work. “It's just a shame that you can't have both at the same time, i.e., make real progress in your own research and travel around all the time,” she says with a laugh. Talking to people who are already further along or have discovered something that is also relevant to her own research is almost her favorite thing to do and motivates her every time. But during a conference, you can't do anything else – “I'd like to have twice as much time!”

Adelina could also use more time outside of her doctoral studies, because the doctoral candidate is very busy in her free time as well. It helps that she still lives in Munich, where she grew up and where the “whole rest of her life” takes place, and commutes to Augsburg three times a week. This gives her time to teach karate to children at her club once or twice a week. “Since last year, I've been teaching a new beginners' class with six-year-olds. It's very cute, but also a whole new challenge,” she says. “You learn a lot about how to keep them under control and at the same time motivate them to try hard on their own.” She says it's particularly important to come across as confident, because then everyone will listen. If, on the other hand, you have too much respect for the task, “then it turns into a huge circus.”

Adelina still trains herself, but she has not been active in competitions since she started college – too much work and pressure for nothing in return “except fame and glory.” But she loves the sport, which trains not only the body but also the mind and is based on a set of values including modesty, honesty, helping others, politeness, courage, and respect. What has helped her most since her school days, however, is the discipline she learned in karate training. “You have to persevere with your training, even beyond your own physical limits,” she explains. “You learn that you can pull yourself together and work on something for longer than you might want to.”

In addition to her activities in the karate club, she is also involved as a mentor in the “Rock your Life” project, where she regularly meets with a 14-year-old student, gives her 77-year-old karate trainer computer lessons, and has been learning Japanese weekly with a tandem partner since a several-month research stay in Japan. In any case, she is never bored – and as long as she feels that she can achieve something good, Adelina knows that she is on the right path for herself. Both privately and professionally.

Published 30 January 2026; Interview 26 November 2025