I’ve always been drawn to understanding how things actually work, but physics was different. It wasn’t just about memorizing formulas—it was about uncovering the fundamental reasons why phenomena occur. Why do atoms vibrate in specific patterns? How do quantum states determine material properties? There’s something deeply satisfying about peeling back layers of complexity to find the elegant principles underneath.

What really hooked me was how everything in physics connects. Thermodynamics links to statistical mechanics, quantum mechanics explains chemistry, and electromagnetism governs how circuits work. Each discovery reveals new questions, and those questions lead you deeper into understanding how the universe actually operates at its core.

Computer science felt familiar in the same way. Starting from electrical signals and building up to entire applications, each layer depending on the ones below it. When I realized that both physics and programming share this same pattern of small, logical pieces creating something much bigger, it clicked for me.

But then I encountered a fascinating intersection: quantum computing. Here was a field that combined everything I loved about physics—the mathematical elegance, the fundamental principles—with the practical power of computation. As classical computers approach their physical limits, quantum computing promises to transcend those boundaries entirely. It’s not just an incremental improvement; it’s a completely different way of processing information based on quantum mechanical principles.

What excites me even more is how this connects to the current AI revolution. Machine learning algorithms are pushing the boundaries of what we can compute, but they’re also hitting walls—the same physical limitations that quantum computing could potentially overcome. I see quantum machine learning as the next logical step: combining quantum computational advantages with AI’s pattern recognition capabilities. It’s like having two powerful paradigm shifts converge into something entirely new.

Research life taught me a lot about myself. The late nights analyzing spectroscopic data, the frustration when experiments didn’t work, the excitement when patterns finally emerged from what seemed like noise. It was tough, but I found I actually enjoyed the process of digging deeper into problems, even when the answers weren’t obvious.

When graduation approached, I had to make a choice: jump into industry or keep studying. Working at a software company showed me the satisfaction of building things that actually work and help people. But it also made something clear—I wasn’t ready to stop learning. The more I worked with real systems, the more questions I had about how we could make them better, especially as we approach the limits of classical computing.

So now I’m looking at graduate school, eventually PhD and postdoc work. Not because I want to avoid the “real world,” but because I think the most interesting problems are still being discovered at the edges where physics meets computation, where quantum mechanics meets practical algorithms.