# Key Concepts¶

- Barren Plateaus¶
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.

- 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.

- Hybrid Computation¶
A computation that includes classical

*and*quantum subroutines, executed on different devices.- Parameter-shift Rule¶
The parameter-shift rule is a recipe for how to estimate gradients of quantum circuits. See also quantum gradient.

- Quantum Approximate Optimization Algorithm (QAOA)¶
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 Convolutional Neural Network¶
A quantum neural network that mirrors the structure of a convolutional neural network. Characterized by alternating convolutional layers, and pooling layers which are effected by performing quantum measurements.

- Quantum Datasets¶
Collections of data for physical systems that exhibit quantum behavior.

- Quantum Differentiable Programming¶
The paradigm of making quantum algorithms differentiable, and thereby trainable. See also quantum gradient.

- 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 Quantum Embedding.

- Quantum Generative Adversarial Network¶
Quantum analog of Generative Adversarial Networks (GANs).

- Quantum Gradient¶
The derivative of a quantum computation with respect to the parameters of a circuit.

- Quantum Machine Learning¶
A research area that explores ideas at the intersection of machine learning and quantum computing.

- 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 variational circuits in the context of quantum machine learning.

- Quantum Node¶
A quantum computation executed as part of a larger hybrid computation.

- Quanvolutional Neural Network¶
A hybrid classical-quantum model in which classical CNNs are augmented by layers of variational quantum circuits.

- Variational Circuit¶
Variational circuits are quantum algorithms that depend on tunable parameters, and can therefore be optimized.

- Variational Quantum Classifier (VQC)¶
A supervised learning algorithm in which variational circuits (QNNs) are trained to perform classification tasks.

- Variational Quantum Eigensolver (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.

- Variational Quantum Linear Solver (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.

- Variational Quantum Thermalizer (VQT)¶
A generalization of the VQE to systems with non-zero temperatures. Uses QHBMs to generate thermal states of Hamiltonians at a given temperature.