Hybrid Data Management Architecture for Present Quantum Computing

TL;DR

A hybrid data management architecture is developed in which databases can serve as data sources for quantum algorithms, and clusters are assigned to data points stored in a database to cluster centroids.

Abstract

Quantum computers promise polynomial or exponential speed-up in solving certain problems compared to classical computers. However, in practical use, there are currently a number of fundamental technical challenges. One of them concerns the loading of data into quantum computers, since they cannot access common databases. In this vision paper, we develop a hybrid data management architecture in which databases can serve as data sources for quantum algorithms. To test the architecture, we perform experiments in which we assign data points stored in a database to clusters. For cluster assignment, a quantum algorithm processes this data by determining the distances between data points and cluster centroids.

Figures (2)

• Figure 1: Hybrid Data Management Architecture [Source: Own representation]
• Figure 2: Distance Estimation Algorithm following Ouedrhiri.2021 with additional encoding of the Tuple IDs to utilize them in post-processing. Parameterized with Data Point ID=3 and Centroid A. Drawn by Qiskit.