Where qubits meet neural networks.
Master quantum computing and quantum machine learning from first principles — with interactive lessons, circuits you run yourself, rigorous assessments and certificates anyone can verify.
- courses
- 2
- courses
- modules
- 23
- modules
- lessons
- 84
- lessons
- of study
- 100h
- of study
Quantum × AI
A quantum circuit you can train.
Quantum machine learning rests on one idea: a circuit with adjustable rotation angles θ is a model. Encode data, entangle, measure, compute a loss, and let a classical optimiser tune θ — exactly like training a neural network.
You'll build every piece of this loop yourself: the linear algebra, the gates, the gradients, and the hybrid classifier that ties them together.
Try it now
Put a qubit into superposition.
This is a real widget from Module 3. Tilt the state with θ, twist its phase with φ, then measure — and watch the Born rule emerge from random outcomes, one shot at a time.
- ◆θ = 90° gives a 50/50 superposition
- ◆Change φ: the Z-basis odds don't move — but the state does
- ◆More shots, less noise: error falls as 1/√N
Qubit explorer
|ψ⟩ = 0.707|0⟩ + 0.707|1⟩
Your path
From |0⟩ to research-grade QML.
Two courses, one progression. Each module builds on the last, ends with an assessment, and unlocks the next.
Foundation
From qubits to your first hybrid quantum classifier
- 01Why Quantum + AI?
- 02The Mathematical Toolkit
- 03Qubits, Superposition and Measurement
- 04Single-Qubit Gates
- 05Multiple Qubits and Entanglement
- 06Circuits in Qiskit and Real Hardware
- 07Oracle Algorithms
- 08Grover's Search
- 09The Quantum Fourier Transform and Phase Estimation
- 10Machine Learning Essentials
- 11First Steps in Quantum Machine Learning
- 12Noise and the NISQ Era
Advanced
Variational algorithms, QML, noise and error correction on real hardware
- 01Density Matrices and Quantum Channels
- 02Shor's Algorithm
- 03Variational Algorithms: VQE and QAOA
- 04Training Parameterised Circuits
- 05Quantum Kernel Methods
- 06Quantum Neural Networks and Hybrid Models
- 07Noise Models and Error Mitigation
- 08Quantum Error Correction
- 09Running on Hardware in Practice
- 10AI for Quantum
- 11Critical Reading of Quantum Claims
From first principles
Every piece of maths is introduced before it's used — complex numbers to variational algorithms, with nothing hand-waved.
Interactive by default
Sliders, Bloch spheres and simulated measurements inside the lesson, plus knowledge checks that gate your progress.
Real circuits, real hardware
Run labs instantly in the browser, on Qiskit Aer with realistic noise, or on IBM quantum computers.
Quantum meets AI
Machine-learning essentials, then hybrid quantum–classical models — with an honest look at where advantage is expected.
Learn at your pace
Pick up exactly where you left off, revisit any lesson, and watch module-by-module progress fill in.
Verifiable certificates
Cryptographically signed credentials with a public verification page, ready to add to your LinkedIn profile.
Credentials that hold up
Earn it. Prove it. Share it.
Certificates are issued only after every module and the final exam are passed. Each one is digitally signed and has a public page, so employers can confirm it's genuine in one click.
Certificate of completion · sample
Ada Lovelace
has completed
Quantum Computing + AI: Foundation
QKarma
Your first qubit is |0⟩ away.
The first module is free. No physics background needed.