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.
.. gallery-item::
:tooltip: Understand the link between variational quantum models and Fourier series.
:figure: demonstrations/expressivity_fourier_series/expressivity_thumbnail.png
:description: :doc:`demos/tutorial_expressivity_fourier_series`
:tags: autograd
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:tooltip: Kernels and alignment training with PennyLane.
:figure: demonstrations/kernels_module/QEK_thumbnail.png
:description: :doc:`demos/tutorial_kernels_module`
:tags: kernels alignment classification
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:tooltip: Kernel-based training with scikit-learn.
:figure: demonstrations/kernel_based_training/scaling.png
:description: :doc:`demos/tutorial_kernel_based_training`
:tags: pytorch sklearn kernels
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:tooltip: A quantum variational classifier.
:figure: demonstrations/variational_classifier/classifier_output_59_0.png
:description: :doc:`demos/tutorial_variational_classifier`
:tags: autograd
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:tooltip: Universal Quantum Classifier with data-reuploading.
:figure: demonstrations/data_reuploading/universal_dnn.png
:description: :doc:`demos/tutorial_data_reuploading_classifier`
:tags: autograd
.. gallery-item::
:tooltip: Quantum transfer learning.
:figure: demonstrations/quantum_transfer_learning/transfer_images.png
:description: :doc:`demos/tutorial_quantum_transfer_learning`
:tags: autograd pytorch
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:tooltip: Create a simple QGAN with Cirq and TensorFlow.
:figure: demonstrations/QGAN/qgan3.png
:description: :doc:`demos/tutorial_QGAN`
:tags: cirq tensorflow
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:tooltip: Fit one-dimensional noisy data with a quantum neural network.
:figure: demonstrations/quantum_neural_net/qnn_output_28_0.png
:description: :doc:`demos/quantum_neural_net`
:tags: autograd strawberryfields photonics
.. gallery-item::
:tooltip: Using a quantum graph recurrent neural network to learn quantum dynamics.
:figure: demonstrations/qgrnn/qgrnn_thumbnail.png
:description: :doc:`demos/tutorial_qgrnn`
:tags: autograd
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:tooltip: Meta-learning technique for variational quantum algorithms.
:figure: demonstrations/learning2learn/l2l_thumbnail.png
:description: :doc:`demos/tutorial_learning2learn`
:tags: tensorflow
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:tooltip: Pre-process images with a quantum convolution.
:figure: demonstrations/quanvolution/zoom.png
:description: :doc:`demos/tutorial_quanvolution`
:tags: tensorflow
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:tooltip: Use multiple QPUs to improve classification.
:figure: demonstrations/ensemble_multi_qpu/ensemble_diagram.png
:description: :doc:`demos/ensemble_multi_qpu`
:tags: pytorch rigetti qiskit
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:tooltip: Generate images with Quantums GANs.
:figure: demonstrations/quantum_gans/patch.jpeg
:description: :doc:`demos/tutorial_quantum_gans`
:tags: pytorch
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:tooltip: Estimate a classical kernel function on a quantum computer.
:figure: demonstrations/classical_kernels/classical_kernels_flow_chart.png
:description: :doc:`demos/tutorial_classical_kernels`
:tags: kernels approximation
.. gallery-item::
:tooltip: Tensor network quantum circuits
:figure: demonstrations/tn_circuits/thumbnail_tn_circuits.png
:description: :doc:`demos/tutorial_tn_circuits`
:tags: tensor network
.. gallery-item::
:tooltip: Quantum advantage in learning from experiments
:figure: demonstrations/learning_from_experiments/learning_from_exp_thumbnail.png
:description: :doc:`demos/tutorial_learning_from_experiments`
:tags: advantage experiments
.. gallery-item::
:tooltip: Machine learning for quantum many-body problems
:figure: demonstrations/ml_classical_shadows/ml_classical_shadow.png
:description: :doc:`demos/tutorial_ml_classical_shadows`
:tags: kernels manybodyphysics classicalml
.. gallery-item::
:tooltip: Train polynomial approximations to functions using QSP.
:figure: demonstrations/function_fitting_qsp/cover.png
:description: :doc:`demos/function_fitting_qsp`
:tags: pytorch
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:tooltip: Generalization in quantum machine learning from few training data
:figure: demonstrations/learning_few_data/few_data_thumbnail.png
:description: :doc:`demos/tutorial_learning_few_data`
:tags: qcnn advantage
.. gallery-item::
:tooltip: Learn how to use symmetries to improve training with equivariant learning
:figure: demonstrations/geometric_qml/equivariant_thumbnail.jpeg
:description: :doc:`demos/tutorial_geometric_qml`
:tags: pytorch geometric qml
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:tooltip: Learn how to quantumly detect anomalous behaviour in time series data with the help of Covalent.
:figure: demonstrations/univariate_qvr/thumbnail_tutorial_univariate_qvr.jpg
:description: :doc:`demos/tutorial_univariate_qvr`
:tags: covalent pytorch
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:tooltip: Train a problem-inspired ansatz on a contextuality-inspired dataset
:figure: demonstrations/contextuality/thumbnail_tutorial_Contextuality.png
:description: :doc:`demos/tutorial_contextuality`
:tags: jax geometric qml inductive bias
.. gallery-item::
:tooltip: See an example of a permutation equivariant graph embedding in PennyLane
:figure: demonstrations/equivariant_graph_embedding/thumbnail_tutorial_equivariant_graph_embedding.png
:description: :doc:`demos/tutorial_equivariant_graph_embedding`
:tags: embedding geometric qml
.. toctree::
:maxdepth: 2
:hidden:
demos/tutorial_expressivity_fourier_series
demos/tutorial_kernels_module
demos/tutorial_kernel_based_training
demos/tutorial_variational_classifier
demos/tutorial_data_reuploading_classifier
demos/tutorial_quantum_transfer_learning
demos/tutorial_QGAN
demos/quantum_neural_net
demos/tutorial_qgrnn
demos/tutorial_learning2learn
demos/tutorial_quanvolution
demos/ensemble_multi_qpu
demos/tutorial_quantum_gans
demos/tutorial_classical_kernels
demos/tutorial_tn_circuits
demos/tutorial_learning_from_experiments
demos/tutorial_ml_classical_shadows
demos/function_fitting_qsp
demos/tutorial_learning_few_data
demos/tutorial_geometric_qml
demos/tutorial_univariate_qvr
demos/tutorial_contextuality
demos/tutorial_equivariant_graph_embedding