A Toolbox to Understand the Physics of Quantum Data Management

TL;DR

This paper introduces a physics-informed computational toolbox for analyzing quantum annealing in data management, aiding understanding of quantum device behavior and problem complexity.

Contribution

It provides a systematic numerical framework to study spectral and dynamical properties of quantum annealing relevant to data management tasks.

Findings

Enables analysis of energy gaps and eigenstates inaccessible via hardware.

Supports interpretation of optimization dynamics and structural similarities.

Facilitates the construction of reduced models for quantum annealing processes.

Abstract

The application of quantum computing to data management has attracted growing interest, yet remains constrained by a limited understanding of how the physical behaviour of quantum devices relates to the structure and difficulty of database problems. In particular, evaluating quantum annealing approaches for combinatorial optimisation, which is central to many data management tasks, poses significant challenges beyond the scope of conventional empirical and complexity-theoretic methods. We present a computational toolbox for the systematic numerical analysis of quantum annealing processes derived from data management problem formulations. Adopting a physics-informed perspective, the toolbox enables the study of spectral and dynamical properties -- such as energy gaps and eigenstate structure -- that are inaccessible through direct hardware measurements, yet essential for understanding computational hardness and scaling behaviour. Our approach further provides derived quantities and visualisation techniques that support the interpretation of optimisation dynamics, the identification of structural similarities to canonical physical models, and the construction of reduced effective descriptions. By bridging methodological gaps between quantum computing and database systems research, this work establishes a principled foundation for evaluating quantum approaches and guiding future co-design efforts.