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.
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.
A computation that includes classical and quantum subroutines, executed on different devices.
The parameter-shift rule is a recipe for how to estimate gradients of quantum circuits. See also quantum gradient.
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.
A research area focused on addressing classically intractable chemistry problems with quantum computing.
A research area that extends the set of physical laws classical computers operate on by accessing quantum aspects of the physical world, opening up new ways of processing information.
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.
Collections of data for physical systems that exhibit quantum behavior.
The paradigm of making quantum algorithms differentiable, and thereby trainable. See also quantum gradient.
Representation of classical data as a quantum state.
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 analog of Generative Adversarial Networks (GANs).
The derivative of a quantum computation with respect to the parameters of a circuit.
A research area that explores ideas at the intersection of machine learning and quantum computing.
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.
A quantum computation executed as part of a larger hybrid computation.
A hybrid classical-quantum model in which classical CNNs are augmented by layers of variational quantum circuits.
Variational circuits are quantum algorithms that depend on tunable parameters, and can therefore be optimized.
A supervised learning algorithm in which variational circuits QNNs are trained to perform classification tasks.
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.
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.
A generalization of the VQE to systems with non-zero temperatures. Uses QHBMs to generate thermal states of Hamiltonians at a given temperature.