I was trained as a scientist. I still work like one.

For as long as I can remember, I have been drawn to how things work. Science and engineering were never just subjects to me — they were the way I made sense of everything around me, the default lens I reached for before any other.

That pull led me to a double Bachelor's degree in Physics and Mathematics. Mathematics gave me a language: precise, universal, the same rules whether you are describing a planet's orbit or the loss surface of a neural network. Physics taught me how to translate what I actually observe into that language, how to turn a messy, real phenomenon into an equation that predicts something true.

Over the five years of my degree, two branches kept pulling harder than the rest: quantum mechanics and machine learning. Different names, same underlying question: how do you reason rigorously about a system you can never fully observe? I spent those years chasing both, and neither one won.

So I went deeper into one of them: a Master's in Quantum Science and Technology, a year spent not closing the question but working at the edge of what we understand, before deciding what to build with it.

What follows is what I did with that. Below is the record of how that thinking turned into engineering, and how research became systems that run in production.