Key Concepts

Automatic Differentiation
Automatically computing derivatives of the steps of computer programs.
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 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 Gradient
The derivative of a quantum computation with respect to the parameters of a circuit.
Quantum Neural Network
A term with many different meanings, usually refering 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.
Variational Circuit
Variational circuits are quantum algorithms that depend on tunable parameters, and can therefore be optimized.