Quantum Computing & Quantum Machine Learning with Python

About this course

This course contains the use of artificial intelligence.

Quantum computing is moving out of the physics lab and onto the developer's laptop. Today you can write a quantum program in Python, run it on a simulator in seconds, and send it to real quantum hardware through the cloud. This course gives you a clear, rigorous and beginner-friendly path from your very first qubit to training hybrid quantum machine learning models.

What makes this course different

From zero to advanced in one path. We start with the big picture and the essential mathematics (complex numbers, vectors, matrices, tensor products), then build up to quantum gates, circuits, entanglement, the major algorithms, real hardware and quantum machine learning.

Code-first and current. Every concept is backed by Python code using Qiskit 1.x (circuits, primitives, Aer simulation, transpilation, Qiskit Runtime) and PennyLane with PyTorch for quantum machine learning.

Honest about what quantum can and cannot do. You will learn to tell real quantum advantage from hype: which speed-ups are exponential, which are quadratic, which are heuristic, and how to benchmark quantum models fairly against strong classical baselines.

Real hardware and real noise. You will run circuits on real quantum processors, measure noise, apply error-mitigation techniques, and understand how quantum error correction works.

Portfolio-ready capstones. Four capstone projects (an algorithm toolkit, a noise-aware hardware experiment, a hybrid quantum classifier and a QAOA portfolio optimiser) give you work you can show employers.

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