Three interns, three projects, and one summer with Quanscient

This summer, three students joined Quanscient, each through a different door.

Mila Huovinen found us at a campus job fair in Tampere. She has since completed two BSc (Tech) degrees and is now in the final year of her master's in computational physics. Melvina Quartey emailed us from Rensselaer Polytechnic Institute in the US, where she is doing her Ph.D. in aeronautical engineering, after following our quantum CFD work for months. Aki Konga, a quantum technology master's student at Aalto University, had crossed paths with us through career events, a course project and hackathon partnership discussions before a master's thesis position came together.

Their projects have been just as different. Mila has been working on machine learning models for Quanscient MultiphysicsAI surrogates. Melvina has taken a new lattice Boltzmann formulation from workflow development to quantum hardware testing. Aki is working on using the iterative QAOA to partition finite element meshes so that engineering problems can be solved in parallel.

But the summer was not only about three technical projects. There were unfamiliar problems, experiments that worked and others that led to dead ends, a week when much of the team gathered in Tampere, and a Stockholm cruise that ended up among all three interns' favorite memories.

Here is how the summer unfolded for each of them.

Mila's summer with Quanscient MultiphysicsAI

Mila Huovinen is spending her summer with us as an AI developer intern.

Mila is in the final year of her master's in computational physics at Tampere University. She has already completed two BSc (Tech) degrees, with a focus on machine learning and computer science. Before joining Quanscient, she had also spent two years working in research groups across a range of fields. Outside work, she describes herself as an outdoorsy person, always open to adventures or side quests.

We first met her at Startup World, a campus job fair in Tampere. The field caught her attention, and she decided to apply for an open position.

Mila came in hoping for varied, challenging work related to her studies. That is largely what she found. As an AI developer intern, she has been working with Quanscient MultiphysicsAI surrogate models and on other problems where machine learning can offer faster ways of finding solutions.

The work has also given her room to explore where different approaches do and do not work. She has deepened her understanding of the use cases and limitations of different machine learning models, learned to use agentic tools more efficiently, and gained experience working as part of a larger software development team. Some things were entirely new, including setting up an MCP server.

One of the less technical lessons has been learning to trust her own judgment more. Over the summer, she has become more comfortable trying unfamiliar things independently and suggesting possible solutions rather than waiting until she is certain.

That independence has been an important part of her experience. Mila describes a working environment where people are available when she has questions, but where she is also trusted to find her own way forward. Some of the most satisfying moments have been seeing that work pay off, implementing a new feature or getting a useful result from an experiment.

The time spent together outside the usual workday mattered too. The summer retreat gave her a chance to get to know colleagues beyond the projects they were working on.

The experience has also given her a possible direction for what comes next. Physics-informed machine learning is something she would like to continue exploring, potentially as a Master's thesis topic at Quanscient.

For someone coming in after her, her advice is simple. Be curious, ask questions, learn through trial and error, and make the most of the people around you.

Mila and Melvina at the go-karting activity during the summer retreat week.

Melvina's quantum research project

Melvina Quartey spent her summer with us as a quantum engineering intern.

Melvina is from Ghana and lives in the US, where she is doing her Ph.D. in aeronautical engineering at Rensselaer Polytechnic Institute.

She found Quanscient while working on quantum CFD algorithms at RPI. Curious about what was happening with the field outside academia, she started looking into industry work in quantum CFD. She followed our work and milestones online for several months before eventually sending an email.

Months later, she was in Tampere.

Before arriving, Melvina hoped for three things. The opportunity to ask a lot of questions, to learn from the team's experience, and to understand more about the thinking and workflows behind quantum algorithm research in an industry setting.

Asked whether those expectations matched reality, her answer was short: “Yes, yes, and yes.”

Getting there still involved some uncertainty. Before coming to Finland, she was nervous about feeling like an outsider. Instead, she found the transition surprisingly smooth. Her tasks were clearly laid out, the resources she needed were available, and she completed her onboarding within a few days.

Her research project covers workflow development through quantum hardware testing for the new One-Step-Simplified Lattice Boltzmann Method. She has been leading the project herself.

Much of that work has been about following an idea far enough to find out whether it works. Melvina says she would often start the day looking forward to picking up wherever she had left off. Her notebook gradually filled with thought processes and changing action items, recording both findings and possible next steps.

Not every path led somewhere. The hardest moments were the ones when the work seemed to reach a dead end. Her way through was usually to leave the problem for a while, come back with fresh eyes, and talk it through with someone else. Sometimes that broke through the wall; other times it made clear that it was time to change direction.

One habit she wants to take with her is what she calls “spelling back” what she has learned after a meeting: explaining her understanding back to the other person to make sure she has actually understood the concept and that everyone is on the same page.

That habit also says something about what she valued in the team. Melvina points to availability and openness. Being able to schedule a discussion, run an idea by someone and know that they are interested in the work. The team's update meetings also gave her a way to follow what everyone else was working on.

Her favorite memory, however, happened away from the research. The summer retreat included her first ever cruise, her first visit to Sweden, and karaoke with the team.

The summer has given her fresh energy for the rest of her Ph.D. and strengthened her interest in research and development in industry. Her advice to future interns is to lean into the community around the work, and to leave some time to explore Tampere.

A summer of quantum algorithms for Aki

Aki Konga is working on his master's thesis with us.

Aki is a quantum technology master's student at Aalto University. Before joining Quanscient, he had worked on quantum material lab measurements, automation for ultracompact imaging sensors at an Aalto startup, and quantum technology commercialization. Those experiences had gradually pulled his interests towards startups, quantum computing and the point where research meets commercial applications.

His connection with Quanscient developed over time. He first came across the company through Aalto University Guild of Physics career events and student excursions. Later, we stayed in contact through a course project, conversations around internship opportunities, and discussions connected to the Junction Quantum Hack, which Aki was organizing.

By then, the overlap between his interests and the work at Quanscient was already quite clear. When he later contacted our Chief Scientist and Co-founder, Ind. Prof. Valtteri Lahtinen about a master's thesis, the timing was right, and he joined the team soon after.

His first week happened to coincide with a rare moment when almost everyone at Quanscient had gathered in Tampere from around the world. The week ended with the Stockholm cruise, giving him an early chance to get to know much of the team beyond day-to-day work.

Aki's thesis focuses on the iterative Quantum Approximate Optimization Algorithm (QAOA) for domain decomposition in finite element analysis. The aim is to partition the meshes describing an engineering problem so that different parts can be solved in parallel using high-performance computing.

The work has also taken him outside Finland. He joined a computational design symposium in Washington DC, where he gathered leads for Quanscient's quantum partnerships and got a closer look at how physics AI, agents and related technologies are developing across the industry.

His thesis gives him considerable independence over what to investigate and where to focus. That freedom also means learning how to lead his own project and decide which questions are worth pursuing.

Aki describes the work as adventurous, particularly because research does not always come with a clear path forward. One of the challenges has been navigating that ambiguity. Deciding what to explore next, testing different directions, and discussing ideas with colleagues when the next step is not obvious. Those conversations have often helped move the work forward.

Aki describes the company culture as ambitious but easy-going. What has mattered most for his thesis work is having people around him who are helpful and constructive, and who are willing to support an idea while it is still taking shape.

Like Mila and Melvina, some of the memories he expects to keep from this period have little to do with an algorithm. The Stockholm cruise and getting to know colleagues outside work stand out, along with the trip to Washington DC.

The experience has pushed him further towards quantum algorithms, multiphysics and industrial R&D. It has also shown him something he had seen less of in university and corporate research: how closely technical work and the business side of a company interact in a startup.

His advice to future interns is not to hesitate to ask for help or ask questions, and to take opportunities to meet colleagues when they come. With people working from different cities and countries, those moments do not happen every day.

Aki at the CDFAM – Computational Design Symposium in Washington, D.C.

Three different projects, one summer

Mila, Melvina and Aki spent the summer working on three very different problems. Machine learning for multiphysics, quantum CFD, and quantum algorithms for finite element analysis.

They also arrived here in three different ways. One met us at a job fair. One followed our work from another continent and sent an email. One got to know Quanscient over time through university events, projects and other points of contact before joining for a master's thesis.

Their days were different too. There were ML experiments, notebooks full of research questions, unfamiliar tools, ambiguous problems, and technical discussions about what to try next. And around all of that were the parts of a summer at work that are harder to put into a project description. Getting to know colleagues, spending time together in Tampere, a retreat, a cruise, karaoke, and the occasional trip further away.

When asked what they would tell the people who come after them, all three ended up somewhere similar.

Ask questions. Try things even when you do not yet know whether they will work. And take the opportunity to get to know the people you are working with.

Three interns, three projects, and, for a while, one summer together.