Topic
10 courses · 9 tutorials
Prof. Elias Fernandez-Combarro Alvarez, University of Oviedo
CERN openlab's free lecture series on quantum computing covering qubits, quantum algorithms, QAOA, VQE, and quantum machine learning. Taught by a University of Oviedo professor with no physics background required.
NVIDIA
NVIDIA's free open curriculum for hybrid quantum-classical computing on CUDA-Q. Interactive notebooks from a first qubit through QEC, chemistry, QAOA, QML and hybrid GPU-QPU workflows. Run locally, on Colab, qBraid, Brev or Braket.
Qubit by Qubit instructors (Stanford PhDs)
A two-semester year-long quantum computing course by The Coding School, taught by Stanford PhDs in partnership with Google Quantum AI. Free for partner schools; scholarships available for individual students.
IonQ
IonQ Researchers
IonQ's free 4-part lecture series on quantum programming. Covers quantum algorithms, circuits, programming with multiple SDKs, and quantum chemistry including VQE. Taught by IonQ researchers.
Udemy
Kumaresan Ramanathan
Udemy course by Kumaresan Ramanathan. Quantum physics, then Qiskit and Q#, including jobs on IBM hardware, and a short introduction to quantum machine learning.
Classiq engineering and research team
Classiq's free tutorials. Algorithms are written in Qmod, their high-level language. Includes machine learning and optimization examples.
edX
Pramey Upadhyaya
Last course in Purdue's Applied Quantum Computing series on edX. QFT, Grover, Shor, then optimization, simulation, chemistry, and machine learning, with cloud hardware.
IBM Quantum
IBM's free ten-hour Quantum Learning course on quantum methods in machine learning, including variational quantum classifiers and quantum neural networks implemented in Qiskit.
QURECA
QURECA's 25-hour advanced QML course, listed with QTIndu. QUBO and D-Wave, parameterised circuits, QAOA, classifiers and clustering. 1,000 EUR on their academy at the time of writing.
IBM Quantum
Dr. Christa Zoufal, Julien Gacon, Dr. David Sutter (IBM Quantum)
Free openHPI course, with IBM Quantum Research, on quantum machine learning: variational classifiers, quantum kernels, and QGANs, in Qiskit.
Run your first differentiable quantum circuit in PennyLane. Build a Bell state, compute gradients, and see why PennyLane is the go-to framework for quantum ML.
Combine a classical pre-trained CNN with a parameterized quantum circuit for image classification using PennyLane's quantum transfer learning technique.
Build your first hybrid quantum-classical machine learning model with TensorFlow Quantum - create a parameterized circuit, wrap it as a Keras layer, and train it with gradient descent.
Build a quantum classifier using PennyLane's parameterized circuits and train it to classify a simple dataset using gradient descent.
A conceptual and practical introduction to quantum machine learning: what QML is, data encoding strategies, parameterized quantum circuits, and a complete classification example.
Learn how to build quantum kernel functions with PennyLane, use them with scikit-learn's SVM, and understand when quantum kernels might offer an advantage over classical kernels, with a full working classification example.
Train a variational quantum classifier on the Iris dataset using PennyLane with the Amazon Braket backend, including local simulation, SV1 managed simulator, and Hybrid Jobs for persistent classical-quantum training loops.
Build a quantum generative adversarial network in PennyLane with a quantum generator and classical discriminator.
Connect PennyLane to Amazon Braket backends to run hybrid quantum-classical workflows, from local simulation to IonQ trapped-ion hardware.