taggit Summary
A typical quantum machine learning model is composed of 2 parts, a classical part for pre- and post-processing data and a quantum part for harnessing the power of quantum mechanics to perform certain calculations easier, such as solving extremely large systems of linear equations. With this, we can finally define a quantum neural network as a variational quantum circuit — a parameterized quantum circuit that can be optimized by training the parameters of the quantum circuit, which are qubit rotations, and the measurement of this circuit will approximate the quantity of interest — i.e. the label for the machine learning task. When we want to encode our classical data into quantum states, we perform certain operations to help us work with the data in quantum circuits. The input data is encoded in a quantum state via a quantum feature map, an encoding strategy that maps data to quantum Hilbert space.