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Key Concepts

.. glossary::

    :doc:`Automatic Differentiation </glossary/automatic_differentiation>`
        Automatically computing derivatives of the steps of computer programs.

    :doc:`Barren Plateaus </demos/tutorial_local_cost_functions>`
        Areas in the cost landscape where the gradient of a parameterized circuit disappears. The mortal enemy of many a variational algorithm,
        the variance of the gradient at these points is also close to zero in all directions.

    :doc:`Circuit Ansatz </glossary/circuit_ansatz>`
        An ansatz is a basic architecture of a circuit, i.e., a set of gates that act on
        specific subsystems. The architecture defines which algorithms a variational circuit can implement by
        fixing the trainable parameters. A circuit ansatz is analogous to the architecture of a neural network.

    :doc:`Hybrid Computation </glossary/hybrid_computation>`
        A computation that includes classical *and* quantum subroutines, executed on different devices.

    :doc:`Parameter-shift Rule </glossary/parameter_shift>`
        The parameter-shift rule is a recipe for how to estimate gradients of quantum circuits.
        See also :doc:`quantum gradient </glossary/quantum_gradient>`.

    :doc:`Quantum Approximate Optimization Algorithm (QAOA) </demos/tutorial_qaoa_intro>`
        A hybrid variational algorithm that is used to find approximate solutions for combinatorial optimization problems. Characterized by a circuit ansatz featuring two alternating parameterized components.

    Quantum Boltzmann Machine
        Quantum analog of a classical `Boltzmann machine <https://en.wikipedia.org/wiki/Boltzmann_machine>`_, in which nodes are
        replaced by spins or qubits. An energy-based quantum machine learning model.

    Quantum Convolutional Neural Network
        A quantum neural network that mirrors the structure of a `convolutional
        neural network
        <https://en.wikipedia.org/wiki/Convolutional_neural_network>`_.
        Characterized by alternating convolutional layers, and pooling layers which
        are effected by performing quantum measurements.

    Quantum Differentiable Programming
        The paradigm of making quantum algorithms differentiable, and thereby trainable. See also
        :doc:`quantum gradient </glossary/quantum_gradient>`.

    :doc:`Quantum Embedding </glossary/quantum_embedding>`
        Representation of classical data as a quantum state.

    Quantum Feature Map
        The mathematical map that embeds classical data into a quantum state. Usually executed by a variational
        quantum circuit whose parameters depend on the input data. See also
        :doc:`Quantum Embedding </glossary/quantum_embedding>`.

    :doc:`Quantum Generative Adversarial Network </demos/tutorial_QGAN>`
        Quantum analog of `Generative Adversarial Networks <https://en.wikipedia.org/wiki/Generative_adversarial_network>`_ (GANs).

    :doc:`Quantum Gradient </glossary/quantum_gradient>`
        The derivative of a quantum computation with respect to the parameters of a circuit.

    :doc:`Quantum Machine Learning <whatisqml>`
        A research area that explores ideas at the intersection of machine learning and quantum computing.

    :doc:`Quantum Neural Network </glossary/quantum_neural_network>`
        A term with many different meanings, usually referring to a generalization of artificial neural
        networks to quantum information processing. Also increasingly used to refer to :doc:`variational circuits </glossary/variational_circuit>` in the context of quantum machine learning.

    Quantum Node
        A quantum computation executed as part of a larger :doc:`hybrid computation </glossary/hybrid_computation>`.

    :doc:`Quanvolutional Neural Network </demos/tutorial_quanvolution>`
        A hybrid classical-quantum model in which classical :abbr:`CNNs
        (Convolutional Neural Networks)` are augmented by layers of variational
        quantum circuits.

    :doc:`Variational Circuit </glossary/variational_circuit>`
        Variational circuits are quantum algorithms that depend on tunable parameters, and can therefore be optimized.

    :doc:`Variational Quantum Classifier (VQC) </demos/tutorial_variational_classifier>`
        A supervised learning algorithm in which variational circuits (:abbr:`QNNs (Quantum Neural Networks)`) are trained to perform classification tasks.

    :doc:`Variational Quantum Eigensolver (VQE) </demos/tutorial_vqe>`
        A variational algorithm used for finding the ground-state energy of a quantum system. The VQE is a hybrid algorithm that involves incorporating measurement results obtained from a quantum computer running a series of variational circuits into a classical optimization routine in order to find a set of optimal variational parameters.

    :doc:`Variational Quantum Linear Solver (VQLS) </demos/tutorial_vqls>`
        An algorithm for solving systems of linear equations on quantum computers. Based on short variational circuits, it is amenable to running on near-term quantum hardware.

    :doc:`Variational Quantum Thermalizer (VQT) </demos/tutorial_vqt>`
        A generalization of the :abbr:`VQE (Variational Quantum Eigensolver)` to systems with non-zero temperatures. Uses :abbr:`QHBMs (Quantum Hamiltonian-Based Models)` to generate thermal states of Hamiltonians at a given temperature.


.. toctree::
    :maxdepth: 2
    :hidden:

    /glossary/automatic_differentiation
    /glossary/circuit_ansatz
    /glossary/hybrid_computation
    /glossary/parameter_shift
    /glossary/quantum_embedding
    /glossary/quantum_gradient
    /glossary/quantum_neural_network
    /glossary/variational_circuit