About
I'm a physicist working at the intersection of two fields that famously nobody understands: quantum computing and machine learning. Since May 2024 I've been a doctoral researcher at the German Cancer Research Center (DKFZ), or more precisely at the Helmholtz Institute for Translational Oncology (HI-TRON) in Mainz.
The question I'm chasing is this: give the same immunotherapy to two patients with the same diagnosis, and one of them gets dramatically better while the other barely responds at all. Nobody can reliably say in advance which is which. The answer is hiding somewhere in the data — in tumours, immune cells and genes — it is just exceptionally well hidden. My job is to build models that go looking for it, and to keep them honest when they'd rather report a pattern that isn't there.
Quantum Machine Learning
A classical bit is a decisive little thing: 0 or 1, pick one. A qubit refuses to commit until you ask it directly, and it can be entangled with other qubits, so asking one of them tells you something about the other. Physicists have spent a century arguing about what that actually means, and engineers have spent thirty years trying to build one that survives longer than a millisecond.
Quantum machine learning asks the obvious follow-up question: given these strange machines, can we learn anything with them? It turns out that most of the classical toolbox has a quantum cousin by now — there are quantum kernels standing in for support vector machines, variational circuits playing the role of neural networks, quantum versions of generative models, and quite a few ideas that don't have a classical counterpart at all. My own favourites are the quantum kernels.
Does it beat classical machine learning? Sometimes, on some problems, if you squint and choose your dataset carefully — which is the polite way of saying that nobody knows yet.
Contact
- Email: florian.heininger@dkfz-heidelberg.de
- ORCID: 0009-0006-4213-2599
- LinkedIn: florian-heininger-qml
- DKFZ · HI-TRON Mainz, Germany