Approximate Quantum Random Access Memory Architectures

TL;DR

This paper introduces an approximate QRAM architecture based on parametric quantum circuits, enabling data storage and retrieval for quantum algorithms without requiring complex qutrit technology.

Contribution

It proposes a novel PQC-based QRAM design that simplifies implementation and demonstrates applications in data storage and machine learning datasets.

Findings

PQC-based QRAM can store binary data effectively.

The architecture supports storage of ML datasets for classification tasks.

It offers a practical alternative to existing QRAM proposals.

Abstract

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.