Quantum Computing (QC) is a research field that has been in the limelight in recent years. In fact, many researchers and practitioners believe that it can provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks that may require years of high-performance computers. In Information Retrieval (IR) and Recommender Systems (RS) we are required to process very large and heterogeneous amounts of data by means of complex operations, often combinatorial in nature. It is thus natural to wonder whether QC could be applied to boost their performance from both the efficiency and the effectiveness point of view.
In this talk I will describe the beginning of a journey into QC from the perspective of a non-QC specialist, who is learning how these technologies can be applied to core IR problems like feature selection or clustering and who is discovering how the barriers for accessing them are lower and lower, making them within everyone’s reach. I will also introduce QuantumCLEF (https://qclef.dei.unipd.it/
), a lab which we will run at CLEF 2024 to engage the community in designing, developing, and evaluating approaches for QC for information access.