Solve your predictive maintenance with HQNNTransparent, Reproducible Results

Utilize HQNN for binary classification in predictive maintenance, processing sensor data through classical preprocessing and embedding via angle encoding into a variational quantum circuit. Features control rotations (R_x, R_y, R_z), creating a data-dependent unitary U_\phi(x) mapping to |\psi(x)\rangle. Variational layers with rotations and entangling gates (\mathrm{CNOT}/\mathrm{CZ}) enhance correlations. Measurements yield expectations fed to a classical neural network, trained end-to-end with gradient methods.

HQNN integrates a parameterize quantum circuit into a classical pipeline. After preprocessing and classical feature extraction, the quantum layer provides an additional representation via angle encoding and variational layers, with measurements passed to a classical head for binary classification of machine failures.