demos_community.rst

Community

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Have an existing GitHub repository or Jupyter notebook showing off quantum machine learning with PennyLane? Read the guidelines and submission instructions here, and have your demonstration and research featured on our community page.

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.. community-card::
    :title: Characterizing the loss landscape of variational quantum circuits
    :author: Patrick Huembeli and Alexandre Dauphin
    :date: 30/09/2020
    :code: https://github.com/PatrickHuembeli/vqc_loss_landscapes
    :paper: https://arxiv.org/abs/2008.02785
    :color: heavy-rain-gradient

    Using PennyLane and complex PyTorch, we compute the Hessian of the loss function of VQCs and
    show how to characterize the loss landscape with it. We show how the Hessian can be used to
    escape flat regions of the loss landscape.

.. community-card::
    :title: Angle embedding in Iris classification with PennyLane's KerasLayer
    :author: Hemant Gahankari
    :date: 09/11/2020
    :code: https://colab.research.google.com/drive/13PvS2D8mxBvlNw6_5EapUU2ePKdf_K53#scrollTo=1fJWDX5LxfvB
    :color: heavy-rain-gradient

    Using angle embedding from PennyLane, this demonstration aims to explain
    how to pass classical data into the quantum function and convert it to
    quantum data. It also shows how to create a PennyLane KerasLayer from a
    QNode, train it and check the performance of the model.

.. community-card::
    :title: Amplitude embedding in Iris classification with PennyLane's KerasLayer
    :author: Hemant Gahankari
    :date: 09/11/2020
    :code: https://colab.research.google.com/drive/12ls_GkSD2t0hr3Mx9-qzVvSWxR3-N0WI#scrollTo=4PQTkXpv52vZ
    :color: heavy-rain-gradient

    Using amplitude embedding from PennyLane, this demonstration aims to explain
    how to pass classical data into the quantum function and convert it to quantum
    data. It also shows how to create a PennyLane KerasLayer from a QNode, train it
    and check the performance of the model.

.. community-card::
    :title: Linear regression using angle embedding and a single qubit
    :author: Hemant Gahankari
    :date: 09/11/2020
    :code: https://colab.research.google.com/drive/1ABVtBjwcGNNIfmiwEXRdFdZ47K1vZ978?usp=sharing
    :color: heavy-rain-gradient

    In this example, we create a hybrid neural network (mix of classical and
    quantum layers), train it and get predictions from it. The data set
    consists of temperature readings in degrees Centigrade and corresponding
    Fahrenheit. The objective is to train a neural network that predicts
    Fahrenheit values given Centigrade values.

.. community-card::
    :title: Using a Keras optimizer for Iris classification with a QNode and loss function
    :author: Hemant Gahankari
    :date: 09/11/2020
    :code: https://colab.research.google.com/drive/17Qri3jUBpjjkhmO6ZZZNXwm511svSVPw?usp=sharing
    :color: heavy-rain-gradient

    Using PennyLane, we explain how to create a quantum function and train a
    quantum function using a Keras optimizer directly, i.e., not using a Keras
    layer. The objective is to train a quantum function to predict classes of
    the Iris dataset.

.. community-card::
    :title: Trainable Quanvolutional Neural Networks
    :author: Denny Mattern, Darya Martyniuk, Fabian Bergmann, and Henri Willems
    :date: 26/11/2020
    :code: https://github.com/PlanQK/TrainableQuantumConvolution
    :color: heavy-rain-gradient

    We implement a trainable version of Quanvolutional Neural Networks using parametrized
    <code>RandomCircuits</code>. Parameters are optimized using standard gradient descent. Our code is based on
    the <a href="https://pennylane.ai/qml/demos/tutorial_quanvolution.html">Quanvolutional Neural
    Networks</a> demo by Andrea Mari. This demo results from our research as part of the <a
    href="https://www.planqk.de">PlanQK consortium</a>.

.. community-card::
    :title: Quantum Machine Learning Model Predictor for Continuous Variables
    :author: Roberth Saénz Pérez Alvarado
    :date: 16/12/2020
    :code: https://github.com/roberth2018/Quantum-Machine-Learning/blob/main/Quantum_Machine_Learning_Model_Predictor_for_Continuous_Variable_.ipynb
    :color: heavy-rain-gradient

    According to the paper "Predicting toxicity by quantum machine learning"
    (Teppei Suzuki, Michio Katouda 2020), it is possible to predict continuous variables—like
    those in the continuous-variable quantum neural network model described in Killoran et al.
    (2018)—using 2 qubits per feature. This is done by applying encodings, variational circuits,
    and some linear transformations on expectation values in order to predict values close to
    the real target.
    Based on an <a href="https://pennylane.ai/qml/demos/quantum_neural_net.html">example</a>
    from PennyLane, and using a small dataset which consists of a one-dimensional feature
    and one output (so that the processing does not take too much time), the algorithm showed
    reliable results.

.. community-card::
    :title: A Quantum-Enhanced LSTM Layer
    :author: Riccardo Di Sipio
    :date: 18/12/2020
    :code: https://github.com/rdisipio/qlstm/blob/main/POS_tagging.ipynb
    :blog: https://towardsdatascience.com/a-quantum-enhanced-lstm-layer-38a8c135dbfa
    :color: heavy-rain-gradient

    In Natural Language Processing, documents are usually presented as sequences of words.
    One of the most successful techniques to manipulate this kind of data is the Recurrent
    Neural Network architecture, and in particular a variant called Long Short-Term Memory
    (LSTM). Using the PennyLane library and its PyTorch interface, one can easily define a
    LSTM network where Variational Quantum Circuits (VQCs) replace linear operations. An
    application to Part-of-Speech tagging is presented in this tutorial.

.. toctree::
    :maxdepth: 2
    :hidden: