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.

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