M.Sc. Ilmo Salmenperä defends his PhD thesis "Investigating Implementation Issues of Quantum Machine Learning" on Friday the 19th of December 2025 at 13 in the University of Helsinki Main building, Auditorium Karolina Eskelin (U3032, Unioninkatu 34, 3rd floor). His opponent is Professor Ilkka Tittonen (Aalto University) and custos Professor Jukka K. Nurminen (University of Helsinki). The defence will be held in English.
The thesis of Ilmo Salmenperä is a part of research done in the Department of Computer Science and in the Empirical Software Engineering group at the University of Helsinki. His supervisor has been Professor Jukka K. Nurminen (University of Helsinki).
Investigating Implementation Issues of Quantum Machine Learning
Quantum Machine Learning (QML), the intersection of quantum computing and machine learning, has been thought to be one of the more promising ways to achieve quantum advantage in the immediate future. Applying these well-defined models in practical settings comes with various restrictions that are dependent on either the underlying hardware or the mathematical formalization of the model itself. In this thesis, we study these two classes of issues, find solutions to them in different contexts, and then generalize the common elements these issues and solutions have.
In the case of hardware-related issues, the key observation is that the hardware features, like qubit counts, gate topology, or error rates, tend to limit the size of the problem we want to solve. These issues can usually be solved by optimizing the model's structure to make it more compatible with the hardware on which it is run. This holds with gate-based approaches, like how we used quantum compilation routines to optimize an existing quantum linear regression routine to be runnable in existing quantum hardware, but also with quantum annealing hardware, like how we leveraged classical software techniques to improve the efficiency of embedding a restricted Boltzmann machine model to a quantum annealing device.
Issues arising from the model construction process tend to be less uniform and are highly dependent on the model itself. The model formalization can give rise to unintended phenomena, which can be alleviated either by accounting for them during use or by adjusting the model itself to negate the issue. We found examples of these issues emerging in existing QML models, such as the phenomenon of self-erasing gates in the established way of constructing quantum embedding kernels, and how the permutation of features can have a considerable impact on the performance of variational QML models. We also developed solutions to these issues in the form of improvements on the models themselves or as best practices on how to circumvent them.
While the research topic of this thesis is incredibly vast, our research is a step towards more practical QML models. It encompasses many distinct QML models and hardware technologies on which these models can be run. By developing insight into how QML algorithms operate in practical settings, we also develop readiness for a possible future in which quantum computers are a viable option for machine learning tasks.
Availability of the dissertation
An electronic version of the doctoral dissertation will be available in the University of Helsinki open repository Helda at
Printed copies will be available on request from Ilmo Salmenperä: