  Recent days have witnessed significant interests in applying quantum-enhanced
techniques for solving a variety of machine learning tasks. Variational methods
that use quantum resources of imperfect quantum devices with the help of
classical computing techniques are popular for supervised learning. Variational
quantum classification (VQC) is one of such methods with possible quantum
advantage in using quantum-enhanced features that are hard to compute by
classical methods. Its performance depends on the mapping of classical features
into a quantum-enhanced feature space. Although there have been many
quantum-mapping functions proposed so far, there is little discussion on
efficient mapping of discrete features, such as age group, zip code, and
others, which are often significant for classifying datasets of interest. We
first introduce the use of quantum random-access coding (QRAC) to map such
discrete features efficiently into limited number of qubits for VQC. In
numerical simulations, we present a range of encoding strategies and
demonstrate their limitations and capabilities. We experimentally show that
QRAC can help speeding up the training of VQC by reducing its parameters via
saving on the number of qubits for the mapping. We confirm the effectiveness of
the QRAC in VQC by experimenting on classification of real-world datasets with
both simulators and real quantum devices.
