7 courses · 9 tutorials
QUANTUM CIRCUITS
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.
QUANTUM ALGORITHM DESIGN
Classiq engineering and research team
Classiq's free library of hands-on quantum computing tutorials covering algorithm design, quantum machine learning, and optimization using the high-level Qmod language. No circuit assembly required.
Udemy
Kumaresan Ramanathan
Udemy's bestselling quantum computing course, created by Kumaresan Ramanathan. Builds from quantum physics fundamentals to quantum programming with IBM Qiskit and Microsoft Q#, running programs on real quantum computers, plus an introduction to quantum machine learning.
IBM Quantum
Dr. Christa Zoufal, Julien Gacon, Dr. David Sutter (IBM Quantum)
Free openHPI course on quantum machine learning developed with IBM Quantum Research. Covers variational quantum classifiers, quantum kernel methods, QGANs, and hands-on implementation in Qiskit.
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.
edX
Pramey Upadhyaya
The algorithms-and-software course closing Purdue University's Applied Quantum Computing series. Covers the quantum Fourier transform, search and Shor's algorithm, plus modern algorithms for optimization, simulation, chemistry, and machine learning, with hands-on cloud hardware access.
QUBITS
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.
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.