Begin your journey into PennyLane and Quantum Machine Learning (QML) by exploring the tutorials below.
The Getting started section is a great place to start if you've just discovered PennyLane and QML and want to learn more about the basics. Or venture straight into the Applications section and explore how to implement trainable circuits for popular applications such as Variational Quantum Eigensolvers and Quantum Chemistry, using either simulators or near-term quantum hardware.
Here you can discover the basic tools needed to use PennyLane through simple demonstrations. Learn about training a circuit to rotate a qubit, machine learning tools to optimize quantum circuits, and introductory examples of photonic quantum computing.
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:tooltip: Use quantum machine learning to rotate a qubit.
:figure: demonstrations/qubit_rotation/bloch.png
:description: :doc:`demos/tutorial_qubit_rotation`
:tags: autograd
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:tooltip: Use quantum machine learning to tune a beamsplitter.
:figure: demonstrations/gaussian_transformation/gauss-circuit.png
:description: :doc:`demos/tutorial_gaussian_transformation`
:tags: autograd photonics
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:tooltip: Use quantum machine learning in a multi-device quantum algorithm.
:figure: demonstrations/plugins_hybrid/photon_redirection.png
:description: :doc:`demos/tutorial_plugins_hybrid`
:tags: autograd photonics strawberryfields
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:tooltip: Compare the parameter-shift rule with backpropagation.
:figure: demonstrations/tutorial_backprop_thumbnail.png
:description: :doc:`demos/tutorial_backprop`
:tags: tensorflow autograd
Get familiar with more advanced applications of PennyLane and quantum machine learning in this section. Learn how to implement a variational quantum eigensolver, play around with quantum chemistry simulations, solve graph problems such as MaxCut, or implement quantum machine learning circuits on real near-term hardware.
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:tooltip: Explore quantum chemistry in PennyLane.
:figure: demonstrations/quantum_chemistry/water_structure.png
:description: :doc:`demos/tutorial_quantum_chemistry`
:tags: chemistry
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:tooltip: Find the ground state of a Hamiltonian.
:figure: demonstrations/variational_quantum_eigensolver/pes_h2.png
:description: :doc:`demos/tutorial_vqe`
:tags: autograd chemistry
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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: Learn how to implement QAOA workflows with PennyLane
:figure: demonstrations/qaoa_module/qaoa_layer.png
:description: :doc:`demos/tutorial_qaoa_intro`
:tags: autograd beginner
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:tooltip: Perform QAOA for MaxCut.
:figure: demonstrations/qaoa_maxcut/qaoa_maxcut_partition.png
:description: :doc:`demos/tutorial_qaoa_maxcut`
:tags: autograd
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:tooltip: Do arbitrary state preparation on a real quantum computer.
:figure: demonstrations/state_preparation/NOON.png
:description: :doc:`demos/tutorial_state_preparation`
:tags: pytorch
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:tooltip: Ising model example with PennyLane PyTorch interface.
:figure: demonstrations/Ising_model/isingspins.png
:description: :doc:`demos/tutorial_isingmodel_PyTorch`
:tags: pytorch autograd
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:tooltip: Extend PyTorch with real quantum computing power.
:figure: demonstrations/pytorch_noise/bloch.gif
:description: :doc:`demos/pytorch_noise`
:tags: forest pytorch
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:tooltip: Learn how noise can affect the optimization and training of quantum computations.
:figure: demonstrations/noisy_circuit_optimization/noisy_circuit_optimization_thumbnail.png
:description: :doc:`demos/tutorial_noisy_circuit_optimization`
:tags: cirq
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:tooltip: Evaluate the potential energy surface of H2 with parallel QPUs
:figure: demonstrations/vqe_parallel/vqe_diagram.png
:description: :doc:`demos/tutorial_vqe_parallel`
:tags: chemistry
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:tooltip: Use multiple QPUs to improve classification.
:figure: demonstrations/ensemble_multi_qpu/ensemble_diagram.png
:description: :doc:`demos/tutorial_ensemble_multi_qpu`
:tags: pytorch forest qiskit
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:tooltip: VQE in different spin sectors
:figure: demonstrations/vqe_uccsd_obs/thumbnail_spectra_h2.png
:description: :doc:`demos/tutorial_vqe_uccsd_obs`
:tags: chemistry
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:tooltip: Learn how to create hybrid ML models using Keras
:figure: _static/Keras_logo.png
:description: :doc:`demos/tutorial_qnn_module_tf`
:tags: tensorflow
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:tooltip: Learn how to create hybrid ML models using Torch
:figure: _static/PyTorch_icon.png
:description: :doc:`demos/tutorial_qnn_module_torch`
:tags: pytorch
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:tooltip: Parallelize gradient calculations with Amazon Braket
:figure: _static/pl-braket.png
:description: :doc:`demos/braket-parallel-gradients`
:tags: braket
.. toctree::
:maxdepth: 2
:caption: Basics
:hidden:
demos/tutorial_qubit_rotation
demos/tutorial_gaussian_transformation
demos/tutorial_plugins_hybrid
demos/tutorial_backprop
demos/tutorial_quantum_chemistry
demos/tutorial_vqe
demos/tutorial_variational_classifier
demos/tutorial_qaoa_maxcut
demos/tutorial_state_preparation
demos/tutorial_isingmodel_PyTorch
demos/pytorch_noise
demos/tutorial_noisy_circuit_optimization
demos/tutorial_vqe_parallel
demos/tutorial_ensemble_multi_qpu
demos/tutorial_qaoa_intro
demos/tutorial_qnn_module_tf
demos/tutorial_qnn_module_torch
demos/braket-parallel-gradients