The emerging field of Quantum Computing (QC) is attracting considerable research interest due to its potential. It is in fact believed that QC could revolutionize the way we approach complex problems by significantly reducing the time required to solve them. Although QC is still in its early stages of development, certain problems can already be addressed using quantum computers, offering a glimpse into its capabilities. The goal of the QuantumCLEF lab is to raise awareness of QC and to design, develop, and evaluate new QC algorithms aimed at solving challenges typically encountered in the implementation of Information Retrieval (IR) and Recommender Systems (RS). Furthermore, the lab provides a valuable opportunity to engage with QC technologies, which are often difficult to access. In this work, we present an overview of the second edition of QuantumCLEF, a lab focused on applying Quantum Annealing (QA), a specific QC paradigm, to three tasks: Feature Selection for IR and RS systems, Instance Selection for IR systems, and Clustering for IR systems. A total of 44 teams registered for the lab, with 5 teams successfully submitting their runs in accordance with the lab guidelines. Given the novelty of the topics, participants were provided with extensive examples and comprehensive materials to help them understand how QA works and how to program quantum annealers.
The emerging field of Quantum Computing (QC) is attracting considerable research interest due to its potential. It is in fact believed that QC could revolutionize the way we approach complex problems by significantly reducing the time required to solve them. Although QC is still in its early stages of development, certain problems can already be addressed using quantum computers, offering a glimpse into its capabilities. The goal of the QuantumCLEF lab is to raise awareness of QC and to design, develop, and evaluate new QC algorithms aimed at solving challenges typically encountered in the implementation of Information Retrieval (IR) and Recommender Systems (RS). Furthermore, the lab provides a valuable opportunity to engage with QC technologies, which are often difficult to access. In this work, we present an overview of the second edition of QuantumCLEF, a lab focused on applying Quantum Annealing (QA), a specific QC paradigm, to three tasks: Feature Selection for IR and RS systems, Instance Selection for IR systems, and Clustering for IR systems. A total of 44 teams registered for the lab, with 5 teams successfully submitting their runs in accordance with the lab guidelines. Given the novelty of the topics, participants were provided with extensive examples and comprehensive materials to help them understand how QA works and how to program quantum annealers.
The field of Quantum Computing (QC) has gained significant popularity in recent years, due to its potential to provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In both Information Retrieval (IR) and Recommender Systems (RS) we are required to build methods that apply complex processing on large and heterogeneous datasets, it is natural therefore to wonder whether QC could also be applied to boost their performance. The tutorial aims to provide first an introduction to QC for an audience that is not familiar with the technology, then to show 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.
Feature selection, the task of identifying the most informative subset of attributes from a large feature pool, is an NP-hard problem central to Information Retrieval (IR) and Recommender Systems (RS). This paper presents Team SYZYGY’s submission to Task 1 of the QuantumCLEF 2026 Lab, formulating feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem solved on a D-Wave quantum annealer. For Task 1A (IR), we introduce five information-theoretic QUBO formulations that organically balance feature relevance and redundancy without hard cardinality constraints, combining mutual information, conditional mutual information, interaction information, and two normalized weighted mutual information variants. On the MQ2007 benchmark, our best configurations reduce the feature count from 46 to just 21 to 24 while surpassing both the full feature and recursive feature elimination baselines. For Task 1B (RS), a simplified collaborative driven QUBO aligns content based similarity with real user behavior, with the simulated annealer successfully improving over the full feature baseline while the physical quantum annealer is hampered by embedding complications. Across both tasks, quantum hardware completes sampling an order of magnitude faster than simulated annealing, though run to run inconsistencies on the physical QPU remain a key open challenge.
Quantum Computing (QC) is an emerging research field that is attracting significant interest from the scientific community. In fact, it is believed that quantum computers can be employed to solve complex computational problems more efficiently than traditional computers, due to their inherent capabilities of exploring large search spaces very efficiently by leveraging the principles of quantum mechanics, such as superposition, entanglement, and tunnelling. However, quantum computers are still in their early stages of development and their applications are very limited, especially in the field of Information Access (IA). Nevertheless, IA systems often face complex optimization problems that might be solved more efficiently through the usage of quantum computers. This paper outlines the author's PhD objectives in designing new methodologies for the application and evaluation of QC technologies for IA problems. Furthermore, this work provides an overview of the achieved preliminary results and a discussion of possible future research directions.
Using Quantum Computers to solve problems in Recommender Systems that classical computers cannot address is a worthwhile research topic. In this paper, we use Quantum Annealers to address the feature selection problem in recommendation algorithms. This feature selection problem is a Quadratic Unconstrained Binary Optimization(QUBO) problem. By incorporating Counterfactual Analysis, we significantly improve the performance of the item-based KNN recommendation algorithm compared to using pure Mutual Information. Extensive experiments have demonstrated that the use of Counterfactual Analysis holds great promise for addressing such problems.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves.