Have You Seen The Devilish Qiskit Squirrel Sabotaging You
The Struggle for Learning in Qiskit’s New Era
Qiskit has long held a central position in quantum computing education. But…
There is something lurking in the dense thicket of learning Qiskit. You don't see it at first. It hides behind method calls, peeks out between class definitions, and scurries away as soon as you're sure you've found the right parameter format. It is cunning. Sneaky. And if you're trying to familiarize yourself with quantum computers today, you've probably already fallen victim to its tricks.
Not so long ago, Qiskit was an inviting gateway to the quantum world. The tools were raw but accessible, the community vibrant, and the examples numerous. But something has changed. And in the wake of that change, much of what made Qiskit so learnable disappeared. Or was it hidden by a vicious adversary?
What happened? Why did it happen? And who, or what, is to blame?
Qiskit’s Pedagogical Golden Age
Qiskit has long held a central position in quantum computing education. Supported by IBM and a strong community of educators, it has been more than just a development toolkit. It has been a platform for learning, experimenting, and sharing. Countless tutorials, Jupyter notebooks, and lectures have emerged from this ecosystem. These offered learners hands-on opportunities to develop quantum circuits and to understand the essence of quantum algorithms.
One of Qiskit's greatest strengths was the diversity of voices. While the official documentation provided the technical framework, the community brought it to life. Tutorials presented alternative implementations, creative use cases, and intuitive explanations that bridged the gap between theory and application.
In this respect, Qiskit outpaced its peers. Libraries such as Cirq and Pennylane also offered and continue to offer powerful features. But they struggled to offer the same breadth of learning content. If you wanted to learn not just programming with a library, but quantum computing as a whole, Qiskit was the place to start.
Qiskit 1.0: Necessary Progress, Collateral Damage
In February 2024, IBM released Qiskit 1.0, a milestone that was intended to signal maturity and production readiness. From a software perspective, the transition was well justified. After years of organic growth and experimental changes, it was time for structure, stability and long-term reliability.
But progress rarely comes without cost.
The revision of the API made a large part of the existing teaching material unusable. Code examples no longer worked. Community tutorials broke. Even IBM's own Qiskit textbook was archived as it could not survive the scale of the changes. In an instant, the rich educational ecosystem that had been built up over the years was hollowed out.
The intention was clear. Qiskit had to evolve. But the side effects were profound. An entire collection of learning tools was lost without a clear path to recovery.
The recent release of Qiskit 2.0 was less disruptive, but did little to repair the damage either. By this time, the pedagogical framework had collapsed. What remained was the official documentation. It is detailed, exhaustive and undeniably technical.
But for all its precision, the documentation is eerily silent on the most important part of the learning process. It does not describe how to actually use what has been described.
This is the central problem. The documentation tells you what a method does, which parameters it accepts and what it returns. But it rarely shows you how to use it in context.
There are no clear examples. No complete solutions. No applied insights.
Just line after line of cold, clinical reference material.
If you are an experienced developer, you may find your way around at some point. If you are a newcomer? You've lost your way in the undergrowth.
And just when you think you've found the clue—when you've found the class you need, or figured out which function seems to be the right one, something scurries off with the crucial information you need.
That's right. The squirrel has struck again.
A Cumbersome Workaround: Reading the Source
Fortunately, Qiskit is open source. In theory, this means that you can study how functions behave by reading the actual implementation. And in fact, this is often the only way to really understand what an object expects or how a parameter should be structured.
But let's be honest. This is not a realistic expectation for most learners.
Searching through the source code of a complex quantum library is labor-intensive, intimidating, and a far cry from the interactive, example-based learning that made Qiskit successful in the first place. It's like expecting someone to understand literature by studying the schematics of the printing press.
The squirrel may not have buried the information, but it has hidden it in places that only experienced trackers know how to find.
Reconciling Stability with Learnability
The dilemma Qiskit faces is not only a technical one, but also a personal one. For you, the learner, the researcher, the curious mind trying to make sense of this quantum thing, it means something deeper. The path to the future is harder to see.
In its quest for production-ready perfection, Qiskit has made compromises. Structure at the expense of accessibility. Clarity at the expense of long-term stability.
But you are the one who has to deal with the consequences.
You shouldn't feel like you need a degree in computer science just to run a simple variational algorithm. Most of us already believe that we need to be mathematicians and have a PhD in physics to understand all this quantum stuff.
You shouldn't have to read the source code to figure out what a function actually does. And you definitely shouldn't have to guess what a parameter should look like because the documentation forgot to show an example.
Let's be honest. You've probably had the feeling that quantum computing is intimidating. And just when you're about to get your foot in the door of quantum computing, the devilish squirrel shows up—grinning, clever and mischievous—and withholds just the practical code examples you need to get ahead. So now you're supposed to chase scattered breadcrumbs in outdated tutorials and cryptic documentation?
That is not learning. That is torture.
But here's the good news. PyQML teaches quantum machine learning in a hands-on way. It provides practical and working examples of all the important quantum algorithms you need to know. In addition to an easy-to-understand explanation of the course...