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.

Experimental evaluation steers the development of Information Retrieval (IR) systems, and large-scale evaluation campaigns provide the field with a common infrastructure to conduct comparable evaluation exercises. Over the years, tools and platforms have been developed to manage and automate these activities, enhance the reproducibility of conducted experiments and facilitate data sharing. In this context, Evaluation-as-a-Service (EaaS) emerged as an approach to avoid distributing experimental collections, which may contain copyrighted or sensitive data, and instead execute containerised code on that data on remote servers. We propose Kubernetes Infrastructure for Managed Evaluation and Resource Access (KIMERA) as the next step from EaaS into Evaluation-in-the-Cloud (EitC), allowing researchers to directly code and execute their systems through their browsers, requiring only an internet connection. Moreover, recent advancements, such as Large Language Models, or new computing paradigms, such as quantum computers, require external third party services and computational resources. In this respect, KIMERA streamlines and simplifies access to such services on-demand via their APIs. More in detail, KIMERA relies on state-of-the-art containerization and orchestration tools, such as Docker and Kubernetes, to provide a robust, scalable, secure, and fault-tolerant IR evaluation platform. KIMERA monitors and stores all the participants' submissions, accurately keeping track of the resource usage, allowing for evaluating both the efficiency and the effectiveness of the deployed methods. Moreover, all participants can be assigned workspaces sharing the same resources (i.e., CPU and RAM), thus enhancing reproducibility and comparability among systems. Finally, KIMERA has been designed with modularity and extensibility in mind, allowing it to be easily adapted to new use cases and usage scenarios. KIMERA has been developed and adopted in the context of the QuantumCLEF lab, to allow for mixed experiments, comparing approaches running on traditional hardware and on real quantum annealers provided by external companies. KIMERA has also been used as a learning resource to provide Quantum Computing tutorials for IR at major conferences, such as ECIR and SIGIR. The source code of KIMERA is openly available at https://github.com/MjPaxter/KIMERA.

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.