demos_community.yaml

- title: Quantum Variational Rewinding for Time Series Anomaly Detection
  author: Jack S. Baker
  date: 19/01/2023
  code: https://github.com/AgnostiqHQ/QuantumVariationalRewinding
  paper: https://arxiv.org/abs/2210.16438
  color: heavy-rain-gradient
  description: |
    In this demo, we walk through a select number of examples from the paper Quantum Variational Rewinding for Time 
    Series Anomaly Detection. Specifically, we demonstrate the detection of anomalous behaviour in a bivariate 
    cryptocurrency time series data and in synthetically generated univariate time series data. 
    The goal of the tutorial is to have others experiment with the code to create new algorithmic variations, as well 
    as exposing the advantages of using the heterogeneous workflow manager Covalent in quantum machine learning workflows.


- title: SO2 Emission Prediction from Diesel Engines with Quantum Technology (5G)
  author: Emirhan Bulut
  date: 16/11/2022
  code: https://github.com/emirhanai/SO2-Emission-Prediction-from-Diesel-Engines-with-Quantum-Technology-5G
  color: heavy-rain-gradient
  description: |
    A worldwide study has been conducted on the emission values of SO2 gases released from diesel engines in the world 
    (class 1 if it has increased compared to the previous year, class 0 if there has been a decrease compared to the 
    previous year, and class 0 for the starting years). In this research, 5G compatible quantum algorithms were 
    designed by me. A quantum computer was used for the process and the minimum number of qubits was for use on all 
    computers. Finally, the same data was tested with a classical deep neural network and 
    a Random Forest algorithm. Based on test accuracy, the quantum algorithm was found to be 
    more performant than all of them.

- title: Generalization of Quantum Metric Learning Classifiers
  author: Jonathan Kim and Stefan Bekiranov
  date: 09/11/2022
  code: https://github.com/Rlag1998/Embedding_Generalization
  paper: https://doi.org/10.3390/biom12111576
  color: heavy-rain-gradient
  description: |
    This demo is a fork of the previously discontinued Embeddings & Metric Learning demo authored by Maria Schuld and 
    Aroosa Ijaz in 2020. This new demo uses the ImageNet ants/bees image dataset and the UCI ML Breast Cancer 
    (Diagnostic) Dataset to assess the generalization limits and performance of quantum metric learning. 
    Schuld and Ijaz's original code was adapted in numerous ways to attempt to produce good test set results for both 
    datasets. The ants/bees dataset, which had a high number of initial features per sample, did not lead to good 
    generalization. Models generalized best for test data when a fewer number of features per sample were used 
    (as seen in the breast cancer dataset), particularly after feature reduction through principal component analysis. 
    Ultimately, this demo illustrates that quantum metric learning can lead to accurate test set classification given a 
    suitable dataset and appropriate data preparation.


- title: Optimizing a Variational Quantum Circuit via Simulated Annealing
  author: Mahnoor Fatima
  date: 24/08/2022
  code: https://github.com/maxwell04-wq/simulated-annealing-pennylane/blob/main/Simulated_Annealing_Tutorial_Pennylane.ipynb
  paper: https://projecteuclid.org/journals/statistical-science/volume-8/issue-1/Simulated-Annealing/10.1214/ss/1177011077.full
  color: heavy-rain-gradient
  description: |
    In this tutorial, a variational quantum circuit is optimized by using simulated annealing. This algorithm returns a stochastic global optimum for the optimization problem.


- title: Continuous Variable Quantum Classifiers -  MNIST
  author: Sophie Choe
  date: 04/06/2022
  code: https://github.com/sophchoe/Continous-Variable-Quantum-MNIST-Classifiers

  paper: https://github.com/sophchoe/Continous-Variable-Quantum-MNIST-Classifiers/blob/main/MNIST_QNN.pdf
  color: heavy-rain-gradient
  description: |
    We built 8 MNIST dataset classifiers using 2-8 qumodes. This family of MNIST classifiers are classical-quantum hybrid circuits using Keras and PennyLane. The quantum circuit is composed of a data encoding circuit and a quantum neural network circuit as proposed in the paper "Continuous variable quantum neural networks" by Killoran et al. The PennyLane-TensorFlow interface converts the quantum circuit into a Keras layer, and the whole network is treated as a Keras network, to which Keras' built-in loss function and optimizer can be applied for parameter updates. Categorical cross-entropy is used as the loss function and Stochastic Gradient Descent is used for the optimizer.


    Author affiliation: Portland State University, Electrical and Computer Engineering.


- title: Weighted SubSpace VQE to find kth excited state energies
  author: Jay Patel, Siddharth Patel, and Amit Hirpara
  date: 03/07/2022
  code: https://github.com/Jay-Patel-257/Qhack-2022/blob/main/SSVQE.ipynb

  paper: https://arxiv.org/pdf/1810.09434.pdf
  color: heavy-rain-gradient
  description: |
    The variational quantum eigensolver (VQE) is generally used for finding 
    the ground state energy of a given hamiltonian. To find the kth excited 
    state energy of the hamiltonian we need to run the VQE optimization 
    process at least k+1 times. Each time we also need to calculate 
    the hamiltonian again, taking into account the state of the previous iteration. 
    Even after that, the accuracy decreases as the value of k increases.
    
    The Subspace Search VQE (SSVQE) algorithm is used to find the kth excited-state energy of a hamiltonian in 
    two subsequent optimization processes.
    Research on a more generalized version of SSVQE, namely Weighted SSVQE, shows that by using the weights as hyperparameters we can find the 
    kth excited-state energy in just a single optimization process. There are two variants of this algorithm:
    Weighted SSVQE to find kth excited state energy, and weighted SSVQE to find all 
    energies up to the kth excited state.

- title: Implementing a unitary quantum perceptron with quantum computing
  author: Katerina Gratsea
  date: 03/02/2022
  code: https://github.com/KaterinaGratsea/Unitary_quantum_perceptron

  paper: https://iopscience.iop.org/article/10.1209/0295-5075/125/30004
  color: heavy-rain-gradient
  description: |
    Here, we simulate a unitary quantum perceptron with quantum computing. 
    The quantum perceptron can be implemented as a single (fast) adiabatic 
    passage in a model of interacting spins. To demonstrate the learning ability 
    of the quantum perceptron, we train it to perform the XOR gate and discuss 
    its power consumption. Author affiliation: ICFO.


- title: quantum Case-Based Reasoning (qCBR) learning by cases
  author: Parfait Atchade
  date: 12/22/2021
  code: https://github.com/pifparfait/qCBR-learning-by-cases

  paper: https://arxiv.org/abs/2104.00409
  color: heavy-rain-gradient
  description: |
    A supervised classifier is a program that can predict a label (class) for a new 
    input object, based on the value of its attributes and on a training set. 
    The training set consists of labelled data. The main idea of quantum Case-Based Reasoning (qCBR) 
    is to interpret the statement of the problem as an input object, and the solution to the problem as an output (label). 
    Therefore, if we have a series of situations (inputs) with their outcomes (labels), 
    we can train our classifier to determine the solution given a new problem.

- title: Exploring quantum models with a teacher-student scheme
  author: Katerina Gratsea and Patrick Huembeli
  date: 11/22/2021
  code: https://github.com/KaterinaGratsea/Teacher-student_scheme

  paper: https://arxiv.org/abs/2105.01477
  color: heavy-rain-gradient
  description: |
    Using PennyLane, we introduce a teacher-student scheme to systematically compare 
    different Quantum Neural Network (QNN) architectures and to evaluate their relative expressive power. 
    This scheme avoids training with a specific dataset and compares the learning 
    capacity of different quantum models.

- title: Hybrid Neural Network using Data-Reuploading technique
  author: Nikolaos Schetakis (nikschet)
  date: 11/04/2021
  code: https://github.com/nsansen/Quantum-Machine-Learning/blob/main/Pennylane%20DEMO%20v4.ipynb

  paper: https://link.springer.com/content/pdf/10.1038/s41598-022-14876-6.pdf
  color: heavy-rain-gradient
  description: |
    We combine a standard Variational Classifier with a Data-Reuploading Classifier,
    and integrate the resulting QNode as a quantum layer in a Hybrid Neural Network.

- title: Fraud Detection
  author: Sophie Choe
  date: 10/04/2021
  code: https://github.com/sophchoe/Binary_Classification_Pennylane_Keras/blob/main/fraud_detection_Pennylane_Keras.ipynb
  paper: https://github.com/sophchoe/Hybrid-Quantum-Classical-MNIST-Classfication-Model/blob/main/QNN.pdf
  color: heavy-rain-gradient
  description: |
    This is a binary classification hybrid model as proposed in the paper
    "Continuous Variable Quantum Neural Networks", composed of 2 layers of feed forward 
    classical layers and 4 layers of quantum neural network. Using the Pennylane Tensorflow plug-in, 
    the whole network is wrapped as a Keras sequential network, whose parameters are updated via 
    Keras's built in loss function and optimizer.

- title: Quantum-Classical MNIST Classification Model
  author: Sophie Choe
  date: 09/28/2021
  code: https://github.com/sophchoe/Hybrid-Quantum-Classical-MNIST-Classfication-Model
  paper: https://github.com/sophchoe/Hybrid-Quantum-Classical-MNIST-Classfication-Model/blob/main/QNN.pdf
  color: heavy-rain-gradient
  description: |
    Keras-PennyLane hybrid model for MNIST classification, inspired by the "Supervised
    learning with hybrid networks" section of the paper "Continuous-variable
    quantum neural networks".

- title: Hybrid quantum-classical auto encoder
  author: Sophie Choe
  date: 09/28/2021
  code: https://github.com/sophchoe/QML/blob/main/auto_encoder_Pennylane_Keras.ipynb
  paper: https://github.com/sophchoe/Hybrid-Quantum-Classical-MNIST-Classfication-Model/blob/main/QNN.pdf
  color: heavy-rain-gradient
  description: |
    Keras-PennyLane implementation of the hybrid quantum-classical auto encoder proposed in the paper
    "Continous-variable quantum neural networks". The loss function used here is the mean-squared error,
    unlike the paper which requires state vector retrieval.

- title: Quantum circuit learning to compute option prices and their sensitivities
  author: Takayuki Sakuma
  date: 09/16/2021
  code: https://github.com/ta641/option_QCL/blob/master/qclop_tutorial.ipynb
  paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3922040
  color: heavy-rain-gradient
  description: |
    Quantum circuit learning is applied to computing option prices and their sensitivities. 
    The advantage of this method is that a suitable choice of quantum circuit architecture 
    makes it possible to compute the sensitivities analytically by applying parameter-shift rules.

- title: Subspace Search Variational Quantum Eigensolver
  author: Shah Ishman Mohtashim, Turbasu Chatterjee, Arnav Das
  date: 08/23/2021
  code: https://github.com/LegacYFTw/SSVQE
  paper: https://arxiv.org/abs/1810.09434
  color: heavy-rain-gradient
  description: |
    The variational quantum eigensolver (VQE) is an algorithm for searching the
    ground state of a quantum system. The SSVQE uses a simple technique to find
    the excited energy states by transforming the |0⋯0⟩ to the ground state,
    and another orthogonal basis state |0⋯1⟩ to the first excited state and so on.
    As a demonstration, the weighted SSVQE is used to find out the excited states
    of a transverse Ising model with 4 spins and that of the hydrogen molecule.

- title: Quantum PPO/TRPO - LSTMs and memory proximal policy optimization for black-box quantum control
  author: Abhilash Majumder
  date: 08/10/2021
  code: https://colab.research.google.com/drive/1wkZpEpOuZHUdI-vRxQAiDlQD455diFSs?usp=sharing
  color: heavy-rain-gradient
  description: |
    Reinforcement Learning as quantum control leverages quantum hybrid circuits
    (QHC) for creating optimizations on policy networks for Deep RL.
    Policy-gradient-based reinforcement learning (RL) algorithms are well suited
    for optimizing the variational parameters of QAOA in a noise-robust fashion,
    opening up the way for developing RL techniques for continuous quantum control.
    This is advantageous to help mitigate and monitor the potentially unknown
    sources of errors in modern quantum simulators. This demo aims to provide an
    implementation of PPO on policy algorithm with QHC for continuous control.

- title: EVA (Exponential Value Approximation) algorithm
  author: Guillermo Alonso-Linaje
  date: 07/30/2021
  code: https://github.com/KetpuntoG/EVA_Tutorial/blob/main/EVA.ipynb
  paper: https://arxiv.org/abs/2106.08731
  color: heavy-rain-gradient
  description: |
    VQE is currently one of the most widely used algorithms for optimizing
    problems using quantum computers. A necessary step in this algorithm is
    calculating the expectation value given a state, which is calculated by
    decomposing the Hamiltonian into Pauli operators and obtaining this value for
    each of them. In this work, we have designed an algorithm capable of figuring
    this value using a single circuit. A time cost study has been carried out, and
    it has been found that in certain more complex Hamiltonians, it is possible to
    obtain a good performance over the current methods.

- title: Meta-Variational Quantum Eigensolver
  author: Nahum Sá
  date: 03/27/2021
  code: https://github.com/nahumsa/pennylane-notebooks/blob/main/Meta-VQE%20Pennylane.ipynb
  color: heavy-rain-gradient
  description: |
    In this tutorial I follow the <a href="https://arxiv.org/abs/2009.13545" target="_blank">Meta-VQE paper</a>. 
    The Meta-VQE algorithm is a variational quantum algorithm that is suited for NISQ devices 
    and encodes parameters of a Hamiltonian into a variational ansatz. We can obtain good 
    estimations of the ground state of the Hamiltonian by changing only those encoded parameters.

- title: Feature maps for kernel-based quantum classifiers
  author: Semyon Sinchenko
  date: 03/03/2021
  code: https://github.com/SemyonSinchenko/PennylaneQuantumFeatureMaps
  color: heavy-rain-gradient
  description: |
    In this tutorial we implement a few examples of feature maps for kernel based quantum
    machine learning. We'll see how quantum feature maps could make linear unseparable data
    separable after applying a kernel and measuring an observable. We will
    <a href="https://arxiv.org/abs/1906.10467" target="_blank">follow an article</a>
    and also implement all the kernel functions with PennyLane.

- title: Variational Quantum Circuits for Deep Reinforcement Learning
  author: Samuel Yen-Chi Chen
  date: 03/03/2021
  code: https://github.com/ycchen1989/Var-QuantumCircuits-DeepRL
  paper: https://ieeexplore.ieee.org/abstract/document/9144562
  color: heavy-rain-gradient
  description: |
    This work explores variational quantum circuits for deep reinforcement learning.
    Specifically, we reshape classical deep reinforcement learning algorithms like
    experience replay and target network into a representation of variational quantum
    circuits. Moreover, we use a quantum information encoding scheme to reduce the number of
    model parameters compared to classical neural networks. To the best of our knowledge,
    this work is the first proof-of-principle demonstration of variational quantum circuits
    to approximate the deep Q-value function for decision-making and policy-selection
    reinforcement learning with experience replay and target network. Besides, our
    variational quantum circuits can be deployed in many near-term NISQ machines.

- title: QCNN for Speech Commands Recognition
  author: C.-H. Huck Yang
  date: 02/03/2021
  code: https://github.com/huckiyang/QuantumSpeech-QCNN
  paper: https://arxiv.org/abs/2010.13309
  color: heavy-rain-gradient
  description: |
    We train a hybrid quantum convolution neural network (QCNN) on acoustic data with up to 10,000
    features. This model uses layers of random quantum gates to efficiently encode convolutional
    features. We perform a neural saliency analysis to provide a classical activation mapping to
    compare classical and quantum models, illustrating that the QCNN self-attention model did learn
    meaningful representations. An additional connectionist temporal classification (CTC) loss on
    character recognition is also provided for continuous speech recognition.

- 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
  description: |
    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.

- 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
  description: |
    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.

- 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
  description: |
    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.

- 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
  description: |
    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.

- 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
  description: |
    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>.

- 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
  description: |
    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.

- 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
  description: |
    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.


- 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
  description: |
    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.

- 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
  description: |
    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.


- 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
  description: |
    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.