qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems

Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro

Abstract: Quantum Computing (QC) has been a focus of research for many researchers over the last few years. As a result of technological development, QC resources are also becoming available and usable to solve practical problems in the Information Retrieval (IR) and Recommender Systems (RS) fields. Nowadays IR and RS need to perform complex operations on very large datasets. In this scenario, it could be possible to increase the performance of these systems both in terms of efficiency and effectiveness by employing QC and, especially, Quantum Annealing (QA). The goal of this work is to design a Lab composed of different Shared Tasks that aims to: