Welcome to AQMLator’s documentation!
Machine learning (ML) has reshaped the landscape of applied computer science by enabling algorithms to self-adapt based on data. Equally transformative, quantum computing (QC) has opened new horizons by addressing computationally challenging problems through quantum mechanics. While both fields have individually shown immense potential, their convergence promises unprecedented capabilities. Enter AQMLator - the (A)uto (Q)uantum (M)achine (L)earning platform. Designed for data scientists, both seasoned in quantum computing and newcomers alike, AQMLator simplifies the integration of quantum machine learning (QML) into your workflows. Without the necessity of deep quantum expertise, users can leverage AQMLator to determine the best-fit QML models for their data and even incorporate the proposed variational quantum circuits as adaptable layers in hybrid models.