Quantum Machine Learning is about the right balance

A comprehensive look at parameterized quantum circuits and an glimpse into my writing routine. What could these two things possibly have in common?

You can also read this letter here.

Dear Quantum Machine Learner,

Today's post deals with parameterized quantum circuits (PQCs) and the balance between expressiveness and trainability. Finding the right balance is a key challenge not only in the field of quantum machine learning, but in many areas. Including writing itself as I am still experiencing.

I keep trying to stick to my release schedule, but it's difficult to find the right balance. Every new feature I add takes time to get right. Right now, creating Blackboard-style images is slowing me down the most. I draw them in TikZ (for those interested in my workflow), and although the results are worthwhile, they are very time-consuming. At the same time, I am introducing many new keywords that I still need to explain properly. For example, in today's post, I happened to write a detailed explanation of the term Uϕ​(x).

At the moment, I feel less like I'm operating a simple car antenna and more like I'm managing a large antenna system. It's powerful, but more difficult to control. Still, it's a thousand times better than my old platform, which in this analogy was really the car antenna. Especially in terms of page speed. Hopefully, you've noticed that the new page loads almost instantly, while the old PyQML page loads and loads and loads...

So, without further ado, let's load today's post.

Put up your antennas

—Frank Zickert
Author of PyQML