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.
Differentiable quantum programming
The paradigm of making quantum programs 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.
Hybrid Computation
A computation that includes classical and quantum subroutines, executed on different devices.
(Quantum) Node
A quantum computation executed as part of a larger hybrid computation.
Parameter-shift rule
A parameter-shift rule is a recipe for how to estimate gradients of quantum circuits. See also quantum gradient.
Variational circuit
Variational circuits are quantum algorithms that depend on tunable parameters, and can therefore be optimized.