QuantumCLEF 2024: Overview of the Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF

The emerging field of Quantum Computing (QC) in computational science is attracting significant research interest due to its potential for groundbreaking applications. In fact, it is believed that QC could potentially revolutionize the way we solve very complex problems by significantly decreasing the time required to solve them. Even though QC is still in its early stages of development, it is already possible to tackle some problems using quantum computers and, thus, begin to see its potential. Therefore, the aim of the QuantumCLEF lab is to raise awareness about QC and to develop and evaluate new QC algorithms to solve challenges that are usually faced when implementing Information Retrieval (IR) and Recommender Systems (RS) systems. Furthermore, this lab represents a good opportunity to engage with QC technologies, which are typically not easily accessible due to their early development stage. In this work, we present an overview of the first edition of QuantumCLEF, a lab that focuses on the application of Quantum Annealing (QA), a specific QC paradigm, to solve two tasks: Feature Selection for IR and RS systems, and Clustering for IR systems. There were a total of 26 teams who registered for this lab, and eventually, 7 teams successfully submitted their runs following the lab guidelines. Due to the novelty of the topics, participants were provided with many examples and comprehensive materials to help them understand how QA works and how to program quantum annealers.