From Code Snippets to Quantum Insights: A New Phase for PyQMLIt is not enough to simply explain quantum machine learning. You have to see it in action. This is the first step toward a more interactive reading experience and uncovering the areas where quantum models could ultimately surpass their classical counterparts.September 4, 2025

Dear Quantum Machine Learner,

I just published the second part of my post on the essence of quantum machine learning. This time, I went beyond abstract discussion and added a practical section: a quantum circuit implementation that clearly shows why these circuits are so interesting for machine learning.

At first glance, it may not be obvious why it took so long to prepare this post. The code itself isn't extensive. The real work was weaving the code and text together into a seamless reading experience. Now, when you click on a reference in the article, the corresponding lines in the source code are automatically highlighted. It's only a small step, but it transforms the post from a static explanation into an interactive tutorial.

Naive use of technology does not exploit its full potential.
Figure 1 Naive use of technology does not exploit its full potential.

And this is where it gets interesting. The result of the circuit is not a simple yes-or-no threshold, as one would expect from a classic classifier. Instead, it generates a smooth, periodic curve. An oscillating probability pattern that arises directly from the interference between the computational paths. To replicate this behavior with a classical neural network, you would need layer upon layer of ReLUs just to approximate what the quantum circuit achieves with a single parameter and minimal depth.

Interference patterns don't just look elegant. They encode decision boundaries in a way that classical models find difficult to emulate. Parameterized quantum circuits are not designed as universal tools, but as scalpels. They are special tools that exploit structures where classical methods are overloaded.

But here lies the challenge that remains unresolved. The true advantage only comes into play when the architecture and training are tailored to the structure of real-world problems. Otherwise, quantum machine learning runs the risk of being nothing more than a clever physics demonstration.

This is just the beginning. In the next few posts, I will examine what it takes to move from theoretical elegance to practical advantages. And what types of learning tasks could ultimately tip the scales in favor of quantum models. But for now, I hope you enjoy the search for the essence of quantum machine learning.

Let's tip the scales

—Frank ZickertAuthor of PyQML