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QardEst: Using Quantum Machine Learning for Cardinality Estimation of Join Queries
Classical and learned query optimizers (LQOs) use cardinality estimations as one of the critical inputs for query planning. Thus, accurately predicting the cardinality of arbitrary queries plays a vital role in query optimization. A recent boom in novel ...
Leveraging Quantum Computing for Database Index Selection
We present an approach to solve the NP-hard database index selection problem on a D-Wave 2X adiabatic quantum annealer with over 1000 qubits. One of our main contributions is an efficient algorithm that maps instances of the index selection problem onto ...
Quantum Data Encoding Patterns and their Consequences
The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, in particular for discrete optimisation. While a few groundbreaking algorithmic approaches are known that can provably outperform classical computers, we ...
Constrained Quadratic Model for Optimizing Join Orders
We present a quantum-based approach for the optimization of join orders in database applications. Our approach relies on a hybrid framework, where classical heuristics are combined with a quantum processor to accelerate the search over the set of ...