Topics and Goals

Welcome to QuantumCLEF (qCLEF), an innovative evaluation lab at the intersection of Quantum Computing (QC), Information Retrieval (IR), and Recommender Systems (RS).
In today's data-driven world, IR and RS systems are challenged by the explosive growth of data and the need for computationally intensive algorithms. Quantum Computing offers new possibilities to address these demands, and while Quantum Computing has already seen applications in various domains, its potential remains largely untapped within IR and RS. The emerging field of Quantum IR explores quantum mechanics principles to model IR problems but has yet to explore the practical implementation of IR and RS systems using quantum technologies.
At QuantumCLEF, we focus on Quantum Annealing (QA), a Quantum Computing approach that uses specialized devices to efficiently solve complex optimization problems by leveraging quantum-mechanical effects. Our mission is to explore whether Quantum Annealing can improve the efficiency and effectiveness of IR and RS systems.
QuantumCLEF aims to:

  • Benchmark the performance of Quantum Annealing against traditional approaches in IR and RS;
  • Identify novel formulations for IR and RS algorithms that can leverage Quantum Annealing;
  • Foster a research community dedicated to applying Quantum Computing technologies in IR and RS.

Quantum Annealing is accessible to researchers with or without a background in quantum physics, thanks to user-friendly tools and libraries designed for this paradigm. We invite you to join us in advancing the field and unlocking new capabilities in IR and RS through Quantum Computing.

For Everyone

For each task we propose a baseline that can be a starting point for the participants. In addition we provide some tutorials covering both the usage of the given libraries to interact with D-Wave quantum annealers and the basic understanding of the Annealing process. Participants can also find here a small tutorial covering how to use our infrastructure and 2 notebooks about Feature Selection and Clustering. In this way, participants can learn more about the fundamental concepts underneath the QA paradigm and will be able to explore and experiment new solutions. Participants only need a laptop with internet access and knowledge of the Python programming language!
We have been already granted access to D-WAVE Quantum Annelears through a project granted by CINECA, the Italian largest supercomputing center and one of the most important worldwide. We have already made an agreement that grants us sufficient QPU access time that can be distributed among several participants.



Reproducibility represents a core aspect that we will follow during this Lab. Each team participating in this task has access to its own machine which has given characteristics (e.g., fixed number of CPU cores, fixed amount of RAM...). This ensures that all the teams will work with the same hardware capabilities and the final results achieved by different groups will be easily comparable. In addition, we will set up public repositories that will be used by the teams to publish their own solutions and results. In such a manner, future research groups in this field could use our datasets, results and solutions to benchmark their own approaches against the ones obtained in our Lab. Participants will face further challenges in each task such as developing new problem formulations that account for the limited connectivity of the quantum annealer or splitting the original problems into subproblems. Participants will have access to the D-Wave Problem Inspector in order to both analyze and visualize how the problem has been physically embedded in the Quantum Processing Unit.



Contacts

Here it is possible to find the email addresses of all the organizers of the Lab. Do not hesitate to contact us for any information!