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:
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compare the performance of QA approaches with respect to their counterparts using traditional hardware;
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identify new ways of formulating problems so that they can be solved with quantum annealers;
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allow researchers from to different fields (e.g., Information Retrieval, Operations Research...) to work together and learn more about QA technologies.
This Lab uses the QC resources provided by CINECA, one of the most important computing centers worldwide, thanks to an already met agreement. In addition, we also show a possible implementation of the required infrastructure which uses Docker containers and the Kubernetes orchestrator to ensure scalability, fault tolerance and that can be deployed on the cloud.
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Pasin, A., Ferrari Dacrema, M., Cremonesi, P., Ferro, N. (2023). qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_9
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