Introduction to Quantum Computing (Qubit by Qubit)
Qubit by Qubit instructors (Stanford PhDs)
5 courses · 9 tutorials
Qubit by Qubit instructors (Stanford PhDs)
Prof. Elias Fernandez-Combarro Alvarez, University of Oviedo
Hasso Plattner Institute / IBM Quantum Research
Classiq engineering and research team
IonQ Researchers
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.
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.
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.
Build a quantum classifier using PennyLane's parameterized circuits and train it to classify a simple dataset using gradient descent.
Build a quantum generative adversarial network in PennyLane with a quantum generator and classical discriminator.
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.
Combine a classical pre-trained CNN with a parameterized quantum circuit for image classification using PennyLane's quantum transfer learning technique.
A conceptual and practical introduction to quantum machine learning: what QML is, data encoding strategies, parameterized quantum circuits, and a complete classification example.
Connect PennyLane to Amazon Braket backends to run hybrid quantum-classical workflows, from local simulation to IonQ trapped-ion hardware.