Skip to main content
SpringerLink
Log in

QuantumCLEF - Quantum Computing at CLEF

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14612))

Included in the following conference series:

  • 270 Accesses

Abstract

Over the last few years, Quantum Computing (QC) has captured the attention of numerous researchers pertaining to different fields since, due to technological advancements, QC resources have become more available and also applicable in solving practical problems. In the current landscape, Information Retrieval (IR) and Recommender Systems (RS) need to perform computationally intensive operations on massive and heterogeneous datasets. Therefore, it could be possible to use QC and especially Quantum Annealing (QA) technologies to boost systems’ performance both in terms of efficiency and effectiveness. The objective of this work is to present the first edition of the QuantumCLEF lab, which is composed of two tasks that aim at:

  • evaluating QA approaches compared to their traditional counterpart;

  • identifying new problem formulations to discover novel methods that leverage the capabilities of QA for improved solutions;

  • establishing collaborations among researchers from different fields to harness their knowledge and skills to solve the considered challenges and promote the usage of QA.

This lab will employ the QC resources provided by CINECA, one of the most important computing centers worldwide. We also describe the design of our infrastructure which uses Docker and Kubernetes to ensure scalability, fault tolerance and replicability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

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

  2. 2.

    https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset.

  3. 3.

    http://qwone.com/~jason/20Newsgroups/.

  4. 4.

    https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots.

References

  1. Arthur, D., Date, P.: Balanced k-means clustering on an adiabatic quantum computer. Quantum Inf. Process. 20(9), 294 (2021). https://doi.org/10.1007/s11128-021-03240-8

    Article  MathSciNet  Google Scholar 

  2. Bauckhage, C., Piatkowski, N., Sifa, R., Hecker, D., Wrobel, S.: A QUBO formulation of the k-medoids problem. In: Lernen, Wissen, Daten, Analysen, Berlin, Germany, CEUR Workshop Proceedings, vol. 2454, pp. 54–63, CEUR-WS.org (2019). https://ceur-ws.org/Vol-2454/paper_39.pdf

  3. Burges, C.J.C.: From RankNet to LambdaRank to LambdaMART: an overview. Technical report, Microsoft Research, MSR-TR-2010-82 (2010)

    Google Scholar 

  4. Delilbasic, A., Saux, B.L., Riedel, M., Michielsen, K., Cavallaro, G.: A single-step multiclass SVM based on quantum annealing for remote sensing data classification. arXiv preprint arXiv:2303.11705 (2023)

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  6. Ferrari Dacrema, M., Moroni, F., Nembrini, R., Ferro, N., Faggioli, G., Cremonesi, P.: Towards feature selection for ranking and classification exploiting quantum annealers. In: Proceedings of 45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), pp. 2814–2824. ACM Press, New York (2022)

    Google Scholar 

  7. Fröbe, M., et al.: The information retrieval experiment platform. In: Chen, H.H., Duh, W.J., Huang, H.H., Kato, M.P., Mothe, J., Poblete, B. (eds.) Proceedings of 46th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), pp. 2826–2836. ACM Press, New York (2023)

    Google Scholar 

  8. Glover, F.W., Kochenberger, G.A., Du, Y.: Quantum bridge analytics I: a tutorial on formulating and using QUBO models. 4OR 17(4), 335–371 (2019). https://doi.org/10.1007/s10288-019-00424-y

  9. Melucci, M.: Introduction to Information Retrieval and Quantum Mechanics. The Information Retrieval Series, vol. 35. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48313-8

    Book  Google Scholar 

  10. Nembrini, R., Ferrari Dacrema, M., Cremonesi, P.: Feature selection for recommender systems with quantum computing. Entropy 23(8), 970 (2021)

    Article  MathSciNet  Google Scholar 

  11. Nguyen, T., et al.: MS Marco: a human generated machine reading comprehension dataset. Choice 2640, 660 (2016)

    Google Scholar 

  12. Pasin, A., Ferrari Dacrema, M., Cremonesi, P., Ferro, N.: qCLEF: a proposal to evaluate quantum annealing for information retrieval and recommender systems. In: Arampatzis, A., et al. (eds.) CLEF 2023. LNCS, vol. 14163, pp. 97–108. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42448-9_9

    Chapter  Google Scholar 

  13. Pilato, G., Vella, F.: A survey on quantum computing for recommendation systems. Information 14(1), 20 (2023). https://doi.org/10.3390/info14010020

    Article  Google Scholar 

  14. Potthast, M., Gollub, T., Wiegmann, M., Stein, B.: TIRA integrated research architecture. In: Ferro, N., Peters, C. (eds.) Information Retrieval Evaluation in a Changing World. The Information Retrieval Series, vol. 41, pp. 123–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22948-1_5

    Chapter  Google Scholar 

  15. Qin, T., Liu, T.Y.: Introducing LETOR 4.0 Datasets. arXiv org, Information Retrieval (cs.IR) arXiv:1306.2597 (2013)

  16. van Rijsbergen, C.J.: The Geometry of Information Retrieval. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  17. Uprety, S., Gkoumas, D., Song, D.: A survey of quantum theory inspired approaches to information retrieval. ACM Comput. Surv. (CSUR) 53(5), 1–39 (2020)

    Article  Google Scholar 

  18. Ushijima-Mwesigwa, H., Negre, C.F.A., Mniszewski, S.M.: Graph partitioning using quantum annealing on the d-wave system. CoRR abs/1705.03082 (2017). https://arxiv.org/abs/1705.03082

  19. Willsch, D., Willsch, M., De Raedt, H., Michielsen, K.: Support vector machines on the d-wave quantum annealer. Comput. Phys. Commun. 248, 107006 (2020)

    Article  MathSciNet  Google Scholar 

  20. Wu, Y., Cao, Q., Shen, H., Tao, S., Cheng, X.: INMO: a model-agnostic and scalable module for inductive collaborative filtering. In: SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, pp. 91–101. ACM (2022). https://doi.org/10.1145/3477495.3532000

  21. Zaiou, A., Bennani, Y., Matei, B., Hibti, M.: Balanced k-means using quantum annealing. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7 (2021). https://doi.org/10.1109/SSCI50451.2021.9659997

Download references

Author information

Authors and Affiliations

  1. University of Padua, Padua, Italy

    Andrea Pasin & Nicola Ferro

  2. Politecnico di Milano, Milan, Italy

    Maurizio Ferrari Dacrema & Paolo Cremonesi

Authors
  1. Andrea Pasin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maurizio Ferrari Dacrema
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paolo Cremonesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nicola Ferro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Pasin .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pasin, A., Dacrema, M.F., Cremonesi, P., Ferro, N. (2024). QuantumCLEF - Quantum Computing at CLEF. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56069-9_66

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56068-2

  • Online ISBN: 978-3-031-56069-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation