.. 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