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. 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.
QuantumCLEF - Quantum Computing at CLEF
Pasin A.;Ferro N.
2024
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. 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.Pubblicazioni consigliate
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