Quantum machine learning¶

Delve into the latest exciting research and cutting-edge ideas in quantum machine learning. Implement and run a vast array of different QML applications on your own computer—using simulators from Xanadu, IBM, Google, Rigetti, and many more—or on real hardware devices.

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Quantum models as Fourier series¶

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Training and evaluating quantum kernels¶

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Kernel-based training of quantum models with scikit-learn¶

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Variational classifier¶

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Data-reuploading classifier¶

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Quantum transfer learning¶

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Quantum generative adversarial networks with Cirq + TensorFlow¶

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Function fitting with a photonic quantum neural network¶

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The Quantum Graph Recurrent Neural Network¶

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Learning to learn with quantum neural networks¶

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Quanvolutional Neural Networks¶

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Ensemble classification with Rigetti and Qiskit devices¶

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How to approximate a classical kernel with a quantum computer¶

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Tensor-network quantum circuits¶

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Quantum advantage in learning from experiments¶

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Machine learning for quantum many-body problems¶

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Function Fitting using Quantum Signal Processing¶

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Generalization in QML from few training data¶

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Introduction to Geometric Quantum Machine Learning¶

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Quantum detection of time series anomalies¶

demos_qml

Download Python script

Download Notebook

View on GitHub