MOOC · 12 modules · 6-8 weeks
Quantum Computing
with Qiskit.
From complex numbers to a running portfolio optimizer, in six weeks. Every module ends with executable code you typed, ran, and debugged yourself — not watched.
Course map
KafCade — every module compoundsClick a module to open. Bars show cognitive load (1–5). Only M0 is fully interactive right now — the rest render frontmatter previews and their ARM drills.
Cold Start
Your first Bell state, in 90 seconds of Python.
Complex Numbers, Vectors, Dirac
The math you already know, wearing different clothes.
Qubit, Bloch Sphere, Basis States
The qubit is not a bit. It's a point on a sphere.
Single-Qubit Gates
Five gates. Every quantum program you'll ever read.
Multiple Qubits & Entanglement
Einstein called this "spooky." You will make it happen.
Circuits at Scale
Debug quantum circuits like you debug production Python.
Bernstein-Vazirani
One quantum query beats N classical queries. Watch.
Grover's Algorithm
√N search. The teacher of amplitude amplification.
Variational Circuits
The bridge from theory to now-usable quantum computing.
VQE
Gradient descent for quantum Hamiltonians.
QAOA
Variational for combinatorial optimization.
Capstone — Portfolio Optimization
Build a quantum portfolio optimizer. Ship the memo.
Real Hardware & What's Next
Now run it on a real quantum computer. Then choose what's next.
How the pedagogy works
B + P + D
Every module has a Build, a Practice rep, and a Deploy artifact you keep. No module ends without runnable code.
RRSS
Assessment ladder: Recall → Recognize → Solve → Synthesize. Each rung is worth more SkillOpt points; synthesis unlocks module transitions.
ARM
Every module opens with an Anchor → Retrieve → Master drill. Three correct in a row to advance. Try M1's drill for a taste.
EvoMetaClaw
Your trajectory rewrites the next three modules. Metacognition prompts (M2, M4, M12 in the design) feed the meta-model.