Let's create a quantum neural network model using the feature map just defined, a digital-analog variational ansatz (also explained here) and a simple observable X (0) ⊗ X (1) X (0)⊗X (1). We use …

Dec 13, 2022 · This paper proposes a hybrid quantum-classical algorithm that learns a suitable quantum feature map which separates non linearly separable unlabeled data using a variational quantum …

DMRG is a powerful variational method for finding the ground state and the first few excited states of strongly correlated 1 1 1 D and 2 2 2 D quantum many-body systems.

The feature map is responsible for encoding the input data into the quantum state, while the ansatz is responsible for the variational part of the model. In addition, a third part of the QNN is the …

Nov 15, 2021 · Variational quantum algorithms that are used for quantum machine learning rely on the ability to automatically differentiate parametrized quantum circuits with respect to underlying …

Here, we solve the problem using the QAOA 1 variational algorithm by embedding the QUBO problem weights onto a register as standard for neutral atom quantum devices.

DMRG is a powerful variational method for finding the ground state of 1D and 2D quantum many-body systems. In practice, it can be used to Simulate adiabatic Pulser sequences. find the ground state of …

Parameter handling: by conveniently handling and embedding the two parameter types that Qadence supports: feature and variational (see more details in the previous section).

Finally, fully parameterized Hamiltonians can be created by passing a string to the strength arguments, and used to prefix the name of the variational parameters.

Variational parameter: a trainable parameter which will be automatically picked up by the optimizer. Feature parameter: a non-trainable parameter which can be used to pass input values.