  Quantum supremacy in many applications using well-known quantum algorithms
rely on availability of data in quantum format. Quantum Random Access Memory
(QRAM), an equivalent of classical Random Access Memory (RAM), fulfills this
requirement. However, the existing QRAM proposals either require qutrit
technology and/or incur access challenges. We propose an approximate Parametric
Quantum Circuit (PQC) based QRAM which takes address lines as input and gives
out the corresponding data in these address lines as the output. We present two
applications of the proposed PQC-based QRAM namely, storage of binary data and
storage of machine learning (ML) dataset for classification.
