.. glossary:: :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. :doc:`Quantum Convolutional Neural Network </glossary/qcnn>` 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. :doc:`Quantum Differentiable Programming </glossary/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. :doc:`Quantum Feature Map </glossary/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. :doc:`Quantum Node </glossary/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/circuit_ansatz /glossary/hybrid_computation /glossary/parameter_shift /glossary/qcnn /glossary/quantum_differentiable_programming /glossary/quantum_embedding /glossary/quantum_feature_map /glossary/quantum_gradient /glossary/quantum_neural_network /glossary/quantum_node /glossary/variational_circuit