Sit back and explore quantum machine learning and quantum programming with our curated selection of expert videos.
Guillermo Alonso (Xanadu)
Learn how to solve quadratic unconstrained binary optimization (QUBO) problems in PennyLane.
Alvaro Ballon (Xanadu)
Learn how to model molecules on quantum circuits and then implement some basic calculations using PennyLane.
Isaac De Vlugt (Xanadu)
Have you ever wondered how we can differentiate an actual quantum computer? In this video, we look at hardware-compatible differentiation methods.
Catalina Albornoz (Xanadu)
Understand one of the most fundamental ingredients used in near-term quantum algorithms and quantum machine learning: optimizing quantum circuits.
From simulators to real hardware, there are plenty of devices to access in PennyLane. Learn about some of their key features in this video.
Learn about the fundamental concept behind what makes PennyLane tick: QNodes.
Get PennyLane installed on your computer (Windows, MacOS, or Linux) with this step-by-step guide.
Josh Izaac (Xanadu)
Using a mixture of real Python examples and illustrated diagrams, we show how to not only evaluate, but also differentiate small quantum programs directly on quantum hardware. By extracting the gradients, we can integrate these quantum programs directly into larger differentiable programs in Python, and train/optimize the full (hybrid quantum-classical!) program.
Nathan Killoran (Xanadu)
What would you do with 1000 QPUs? In the latest release of PennyLane, we've introduced a number of new features to make computations involving multiple QPUs more seamless and accessible. See a number of simple ideas and use-cases where many QPUs could provide a benefit over a single QPU, even for today's small and noisy devices.
The Xanadu team hosted its first quantum machine learning hackathon, QHACK, from Nov 25–26, 2019, bringing together experts and enthusiasts in quantum computing and quantum machine learning.
Gavin Crooks (X, The Moonshot Factory)
See how the space of all 2-qubit gates can be mapped to a pyramid—which can be printed and folded for personal reference! By using known 2-qubit gate decompositions, the parameter-shift rule for calculating quantum gradients on hardware can be extended to any 2-qubit unitary operation.
Maria Schuld (Xanadu)
Exploring links between quantum circuits, neural networks, kernel methods, and generative models.
Max Henderson (Rigetti)
Learn about two of the best-named quantum machine learning models—Quantum Kitchen Sinks and Quanvolutional Neural Networks—courtesy of Max Henderson of Rigetti Computing.
A 20-minute crash-course on PennyLane, its features, and how it can be used to streamline quantum machine learning.
Patrick Coles (Los Alamos National Lab)
In order to train quantum circuits, we need to pay careful attention to the cost functions that we choose to optimize, otherwise the training can suffer from barren plateau problems.
An introduction to quantum machine learning for machine learning scientists. Learn what makes quantum computing so different from classical computing, and explore techniques for training and machine learning with quantum computers.
Introducing PennyLane, a Python-based software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.
Explore different aspects of variational quantum machine learning algorithms, including their role in the development of near-term quantum technologies, strategies of automatic differentiation, and how to integrate quantum circuits with machine learning frameworks such as PyTorch and Tensorflow using open-source software.
Explore the relationships between deep learning, differentiable programming, and quantum computing. Learn how quantum circuits can be trained just like neural networks!
Dive into the world of quantum gradients. Learn how to apply the parameter-shift rule, and how derivatives of quantum circuits can be computed automatically in software.
Take a trip to the variational algorithm zoo! Gain a new appreciation for the techniques and challenges involved in implementing methods like the VQE, QAOA, and quantum GANs.
Connect the quantum and classical worlds to build trainable hybrid machine learning models! See how quantum computing and your favourite machine learning libraries can be combined to augment classical techniques.
