A Toolbox to Understand the Physics of Quantum Data Management

2026-05-14

AI Summary

This work addresses the current lack of systematic understanding regarding the relationship between the physical behavior of quantum devices and the structure and hardness of combinatorial optimization problems in database management, which hinders effective evaluation of quantum annealing for data-centric applications. To bridge this gap, the study introduces a physics-informed computational framework that integrates quantum annealing modeling, spectral analysis, dynamical simulation, and eigenstate structure characterization, augmented by physics-inspired dimensionality reduction and visualization techniques. This approach uncovers hardware-inaccessible yet hardness-determining quantum features—such as energy gap evolution—and identifies structural similarities between problem instances and canonical physical models. The resulting framework provides a scalable and interpretable foundation for evaluating and co-designing quantum algorithms tailored to data management tasks.

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.

Problem

• quantum data management
• quantum annealing
• combinatorial optimisation
• computational hardness
• database systems

Innovation

• quantum annealing
• data management
• spectral analysis
• computational hardness
• physics-informed modeling