Topics and Goals

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.


The tutorial will cover theoretical and practical aspects underneath QC (and especially QA) by allowing participants to code and use real quantum annealers to solve optimization problems usually faced by many computer systems, including IR and RS systems. The duration of the tutorial will be half-day (3 hours) plus breaks and it will be subdivided into 4 parts.

Part 1: QC Foundations (40 min)

The first part consists in a gentle introduction to QC, showing its potential benefits but also limitations. We will also delve into the QA paradigm. It comprises:

  • Introduction to QC;
  • Introduction to QA;

Part 2: QUBO Formulation (50 min)

  • How to write NP-complete binary decision problems in QUBO formulation;
  • Feature selection and clustering with QA;
  • Architecture of a Quantum Annealer: number of available qubits and their topology;

Part 3: QC for IR and RS and their Evaluation (20 min)

  • Effectiveness and efficiency;
  • The QuantumCLEF lab;

Part 4: Hands-on (70 min)

This part discusses how to use quantum annealers, which are available as a cloud service. It involves:

  • The QuantumCLEF infrastructure;
  • How to program a Quantum Annealer;
  • Hands-on: feature selection and clustering.

Target Audience

This tutorial is intended for people coming from IR and RS but also from other fields, such as Machine Learning, Big Data, Operations Research, and Optimization. In fact, QC and QA can be applied to solve problems in different domains and, even if the practical part is focused in using QA for IR and RS systems, the considered problems are very general and common to several research areas.

  • Targeted audience: Due to the topic's novelty, the target audience is of researchers and industry practitioners mainly belonging to IR and RS.
  • Prerequisite knowledge: This tutorial will be self-contained and has minimal prerequisite knowledge, mainly consisting on being familiar with the concept of decision and optimization problems. For those interested in the hands-on part, basic Python programming skills are required to interact with quantum annealers through the tools provided by D-Wave.


Here you can find the slides used during the tutorial:

Here you can find the link to the git repository where you can find the notebooks for the Hands-On part: Link to the repository