# Key Concepts¶

- Automatic Differentiation
- Automatically computing derivatives of the steps of computer programs.
- 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 Boltzmann Machine
- Quantum analog of a classical Boltzmann machine, in which nodes are replaced by spins or qubits. An energy-based quantum machine learning model.
- 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 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.

glossary

Download Python script

Download Notebook

View on GitHub

## Downloads

## Related tutorials