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2024 | OriginalPaper | Chapter

Quantum Computing for Information Retrieval and Recommender Systems

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

Published in: Advances in Information Retrieval

Publisher: Springer Nature Switzerland

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Abstract

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. In Information Retrieval (IR) and Recommender Systems (RS) we are required to process very large and heterogeneous datasets by means of complex operations, it is natural therefore to wonder whether QC could also be applied to boost their performance. The goal of this tutorial is to show how QC works to an audience that is not familiar with the technology, as well as how to apply the QC paradigm of Quantum Annealing (QA) to solve practical problems that are currently faced by IR and RS systems. During the tutorial, participants will be provided with the fundamentals required to understand QC and to apply it in practice by using a real D-Wave quantum annealer through APIs.

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previous chapter Explainable Recommender Systems with Knowledge Graphs and Language Models

next chapter Recent Advances in Generative Information Retrieval

https://qclef.dei.unipd.it/.

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Title: Quantum Computing for Information Retrieval and Recommender Systems
Authors: Maurizio Ferrari Dacrema
Andrea Pasin
Paolo Cremonesi
Nicola Ferro
Publisher: Springer Nature Switzerland
Book: Advances in Information Retrieval
Print ISBN: 978-3-031-56068-2

Electronic ISBN: 978-3-031-56069-9

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-56069-9_47

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