Sit back and explore quantum machine learning and quantum programming with our curated selection of expert videos.
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:title: Using Multiple QPUs
:author: 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.
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:title: Welcome to QHACK'19
:author: Nathan Killoran (Xanadu)
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.
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:title: Quantum Gates and Gradients
:author: 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.
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:title: Using Quantum Circuits as Machine Learning Models
:author: Maria Schuld (Xanadu)
Exploring links between quantum circuits, neural networks, kernel methods, and generative models.
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:title: Everything and the (Quantum) Kitchen Sink: Quantum Machine Learning at Rigetti
:author: 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.
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:title: Quantum Machine Learning with PennyLane
:author: Josh Izaac (Xanadu)
A 20-minute crash-course on PennyLane, its features,
and how it can be used to streamline quantum machine learning.
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:title: Barren Plateau Issues for Variational Quantum-Classical Algorithms
:author: 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.
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:title: Machine Learning With Quantum Computers
:author: Maria Schuld (Xanadu)
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.
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:title: PennyLane - Automatic differentiation and machine learning of quantum computations
:author: Josh Izaac (Xanadu)
Introducing PennyLane, a Python-based software framework for optimization and
machine learning of quantum and hybrid quantum-classical computations.
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:title: Innovating machine learning with near-term quantum computing
:author: Maria Schuld (Xanadu)
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.
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:title: Quantum Deep Learning
:author: Nathan Killoran (Xanadu)
Explore the relationships between deep learning, differentiable
programming, and quantum computing. Learn how quantum circuits can be
trained just like neural networks!
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:title: Automatic Differentiation of Quantum Circuits
:author: Nathan Killoran (Xanadu)
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.
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:title: Variational Quantum Algorithms
:author: Nathan Killoran (Xanadu)
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.
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:title: Hybrid Quantum-Classical Machine Learning
:author: Nathan Killoran (Xanadu)
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.