Simulation of quantum circuits with array-like DBMSs

An Empirical Case Study of SciDB versus Relational DBMSs

Bachelor Thesis (2026)
Author(s)

B.P. Faliszewski (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

R. Hai – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)

T.M. Littau – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)

S.D.C. Wehner – Graduation committee member (TU Delft - QID/Wehner Group)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
expand_more
Publication Year
2026
Language
English
Graduation Date
26-06-2026
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
Downloads counter
20
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Quantum circuit simulators running on classical hardware are essential for developing the field while quantum technology matures, but they struggle to scale to the large, memory-intensive states that many circuits produce. Recent work has shown that relational database management systems (RDBMSs) can simulate quantum circuits and that they have an advantage when the workload exceeds the available memory. However, quantum circuits are naturally expressed as tensor contractions, which suggests that array-based database systems - designed to store and operate on multi-dimensional arrays - might be a more natural fit. This possibility has not been systematically evaluated. This paper presents an empirical case study of SciDB, a representative array (tensor-based) DBMS, benchmarked against two relational engines (PostgreSQL and Umbra) on two contrasting circuits: GHZ state preparation, whose states are sparse, and the Quantum Fourier Transform, whose states are dense, across increasing qubit counts. We additionally apply autotuning via MLOS/SMAC to optimise SciDB's configuration. Across all tested cases, SciDB was the slowest engine. The sparse GHZ workload exposes a large fixed per-step overhead, while on the dense QFT, this overhead amortises as the gap to the relational engines narrows from over three orders of magnitude to roughly twofold at 24 qubits against PostgreSQL. Autotuning yielded no improvement over the default configuration, indicating that SciDB's bottleneck lies outside the tuned parameters. We conclude that SciDB offers no advantage over RDBMSs for in-core simulation at these scales, and identify out-of-core simulation - the regime in which database backends are expected to excel - as the central open direction.

Files

CSE3000_Research_Paper.pdf
(pdf | 0.739 Mb)
License info not available