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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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:title: Layerwise learning for quantum neural networks
:author: Felipe Oyarce Andrade
:date: 26/01/2021
:code: https://github.com/felipeoyarce/layerwise-learning
:color: heavy-rain-gradient
In this project we’ve implemented a strategy presented by <a
href="https://arxiv.org/abs/2006.14904" target="_blank">Skolik et al., 2020</a> for
effectively training quantum neural networks. In layerwise learning the
strategy is to gradually increase the number of parameters by adding a few
layers and training them while freezing the parameters of previous layers
already trained. An easy way for understanding this technique is to think
that we’re dividing the problem into smaller circuits to successfully avoid
falling into Barren Plateaus. We provide a proof-of-concept
implementation of this technique in Pennylane’s Pytorch interface for binary
classification in the MNIST dataset.
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:title: A Quantum-Enhanced Transformer
:author: Riccardo Di Sipio
:date: 20/01/2021
:code: https://github.com/rdisipio/qtransformer
:blog: https://towardsdatascience.com/toward-a-quantum-transformer-a51566ed42c2
:color: heavy-rain-gradient
The Transformer neural network architecture revolutionized the analysis of
text. Here we show an example of a Transformer with quantum-enhanced
multi-headed attention. In the quantum-enhanced version, dense layers are
replaced by simple Variational Quantum Circuits. An implementation based on
PennyLane and TensorFlow-2.x illustrates the basic concept.
.. 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.
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: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.
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: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>.
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: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.
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: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: 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: 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: 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.
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