A Practical Introduction to Quantum Computing (CERN)
Prof. Elias Fernandez-Combarro Alvarez, University of Oviedo
7 courses · 9 tutorials
Prof. Elias Fernandez-Combarro Alvarez, University of Oviedo
Classiq engineering and research team
Kumaresan Ramanathan
Dr. Christa Zoufal, Julien Gacon, Dr. David Sutter (IBM Quantum)
IonQ Researchers
Pramey Upadhyaya
Qubit by Qubit instructors (Stanford PhDs)
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.