Quantum computing and database systems
Quantum and quantum-inspired hardware accelerators for database optimization, database techniques for quantum simulation.
Quantum computing has transformed from a purely theoretical concept into a technology that is widely available and improving quickly. Several manufacturers, including IBM and Google, are manufacturing corresponding hardware, and customers can rent computation time on early-stage quantum computers via Cloud offerings such as Amazon Braket.
We are exploring two complementary research directions connecting database systems on quantum computing. First, we leverage hardware accelerators based on quantum computing or related technologies for hard database optimization problems. This includes work on problems such as multiple query optimization, join ordering, data partitioning, and index selection, using hardware such as Fujitsu's digital annealer, and D-Wave's quantum annealer.
Second, we explore ways to apply ideas from the database community in the context of quantum computing. For instance, we are exploring ways to make quantum simulation more efficient on classical machines, exploiting techniques such as query optimization and caching.
Related publications
2026
PVLDB Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation.
Q-Data Leveraging Quantum Annealing for Materialized View Selection.
Q-Data Quantum Annealing for Multiobjective Query Optimization.
SIGMOD Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing.
2025
Q-Data Cost-Based Query Optimization for Quantum Computation.
Q-Data Leveraging Quantum Computing for Optimal Data Allocation in Distributed Systems.
SIGMOD Companion Second Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data).
2024
Q-Data Towards Out-of-Core Simulators for Quantum Computing.
Q-Data Leveraging Quantum Computing for Database Index Selection.