With the development of quantum computing, significant advancements have been made
since the 1980s, leading to the birth of quantum computers and sparking widespread …

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 …

Graph neural networks have shown great utility in streamlining and contextualizing machine
learning on relational databases, due to their ability to automate integration and capture …

Join order optimization is among the most crucial query optimization problems, and its
central position is also evident in the new research field where quantum computing is …

Graph algorithms are at the center of database optimization and data management, and
countless optimization algorithms can be expressed as graph algorithms. Additionally, graph …

Quantum annealing is a meta-heuristic approach tailored to solve combinatorial optimization
problems with quantum annealers. In this tutorial, we provide a fundamental and …

Quantum Natural Language Processing (QNLP) is built on a novel, category theoretical
quantum machine learning model, which we use to solve a well-researched problem in the …