About this site

Where it started

In 2018 I ran my first quantum circuit on IBM's 5-qubit processor through the IBM Quantum Experience. I still remember sitting there watching the measurement histogram come back from actual quantum hardware. Real qubits. A dilution refrigerator in a lab somewhere, returning results that a classical bit literally cannot produce. Superposition collapsing on measurement. Entanglement showing up as perfect correlations in the output. That was it for me.

I'd been fascinated by quantum physics for years before that. The double-slit experiment. Bell's theorem. The measurement problem. The sheer strangeness of the fact that the universe computes this way at the lowest level. Getting to write actual code and watch it run on real quantum hardware made all of that feel viscerally real in a way that reading about it never quite did.

The problem was that the learning resources were, genuinely, awful. Everything was either pop-science hand-waving or PhD-level physics with no on-ramp. I'd spend an hour reading course reviews, open three Coursera tabs, watch someone's YouTube breakdown of a Udemy course, and still not know which one was worth my time. Half the tutorials linked to deprecated Qiskit 0.x code that threw errors the moment I tried to run it.

So I built the site I wanted. One place with honest metadata, working code, and enough content to go from "what even is a qubit" to "I can write and run circuits on real hardware."

The people who made it real

Quantum computing exists because of a handful of people who decided to take quantum physics seriously as a computational model, at a time when most people thought it was an academic curiosity at best.

David Deutsch is the one who started it. His 1985 paper proposing the universal quantum computer was the first serious argument that quantum mechanics could be used to build a fundamentally different kind of computing machine. Not just faster, not just more efficient, but capable of things that are classically impossible in principle. Reading Deutsch is like watching someone realize that the laws of physics are a programming language.

Peter Shor turned the theoretical curiosity into something urgent. His 1994 algorithm showed that a quantum computer could factor large integers in polynomial time, which means it could break the RSA encryption that protects most of the internet. Suddenly this was not just an interesting physics problem. Governments paid attention. Billions of dollars followed. Whether or not you care about cryptography, Shor's algorithm is the reason quantum computing got serious funding and serious engineering.

Lov Grover gave us something more subtle and, in some ways, more beautiful. His 1996 search algorithm showed that a quantum computer can search an unsorted database in O(sqrt(N)) time instead of O(N). The quadratic speedup is not as dramatic as Shor's exponential speedup, but Grover's algorithm applies to almost any search problem and the proof is elegant in a way that makes you want to read it twice just to appreciate it. It was also one of the first hints that quantum advantage does not require exotic structure in the problem.

Why this matters

Quantum computing is genuinely the future of computation for certain classes of problems. Not everything. Classical computers are not going anywhere. But for simulating quantum chemistry, optimizing combinatorial problems, and running cryptographic protocols, a fault-tolerant quantum computer will do things that no classical machine can match regardless of how many transistors you stack.

We are not there yet. Current hardware is noisy. Circuit depth is limited. Error rates are still too high for the deep circuits that algorithms like Shor's require on problem sizes that matter. But the engineering progress over the past decade has been remarkable, and the trajectory is real. IBM went from 5 qubits in 2016 to 1,000+ qubit processors by 2023. Google demonstrated quantum supremacy on a classically intractable task in 2019. Microsoft announced credible Majorana qubit results in 2025. This is not vaporware.

The people learning quantum computing now are the ones who will build what comes next. That is the actual reason I keep updating this site. Not to capture search traffic, not to sell courses. Because I think this stuff matters, and good learning resources for it are still genuinely hard to find.

What's on here

The core of the site is the course catalog: curated listings from Coursera, edX, Udemy, Brilliant, and a few others, with real prices and level labels. The descriptions are written to tell you what you'll actually learn rather than copying the marketing copy.

The tutorials are all written and tested against current versions of the frameworks they cover. When Qiskit 1.0 shipped and broke basically every 0.x tutorial on the internet, the affected content was rewritten. If you find something broken or wrong, emailing is the fastest way to get it fixed.

There's also a glossary with over 240 terms, a hardware comparison guide, an algorithm reference, a salary guide for people thinking about careers in the field, and a few interactive tools (Bloch sphere simulator, a quantum gate pinball game that's more useful than it sounds for building intuition).

The timeline page is probably the most underrated thing on here. It goes from Shor's 1994 paper through Majorana 1 in 2025 and traces how the field actually developed. Good for getting context before diving into the technical material.

A few honest opinions

Frameworks covered

Every framework page has an install reference, quick-start code, and links to the tutorials that use it.

Affiliate disclosure

Paid courses use affiliate links (Coursera, edX, Udemy, Brilliant). If you buy a course through a link on this site I earn a small commission. This does not change which courses are listed or recommended. I'd rather point you to a free edX audit than a paid Udemy course if the free one is better. The tutorials, glossary, tools, and reference material are all free with no upsell.

Get in touch

If a tutorial has broken code, a course link is dead, there's a factual error, or you want to suggest a topic that should be covered:

Everything gets read. Factual corrections happen quickly. New content suggestions go into a backlog that I do actually work through.