Publications

Towards Reproducible Evaluation of Distributed Quantum Circuit Partitioning Algorithms

J. Vela-Tambo, D. Azizov, T. Guo

TLDR: Distributed Quantum Computing (DQC) connects modular Quantum Processing Units (QPUs) to overcome the scaling limits of monolithic processors. Executing algorithms across QPUs requires circuit partitioning while minimizing inter-QPU communication. Existing evaluations focus mainly on entanglement cost and overlook structural and temporal overheads. We address this gap by applying established benchmarking metrics to partitioned circuits. Using an open-source evaluation pipeline, we assess partitioning algorithms across standard workloads and network topologies. Results show that algorithms with similar entanglement costs can cause very different execution penalties, including deeper circuits and lower gate density. These findings highlight the need for comprehensive circuit-level metrics when designing DQC compilers.

Circuit Partitioning, Distributed Quantum Computing, Quantum Benchmarking

CienaLLM: Generative Climate-Impact Extraction from News Articles with Autoregressive LLMs

J. Vela-Tambo, J. Gracia, F. Dominguez-Castro

TLDR: Master Thesis for Research in Artificial Intelligence at the Universidad Internacional Menéndez Pelayo (UIMP). This thesis presents CienaLLM, a novel approach for extracting structured climate-impact information from unstructured news articles using autoregressive large language models (LLMs). The study evaluates the effectiveness of LLMs in identifying and classifying climate-related events, demonstrating their potential to enhance climate impact analysis and support decision-making processes.

Large Language Models, Master Thesis, Natural Language Processing

SeqIA: A Python framework for extracting drought impacts from news archives

M. López-Otal, F. Domínguez-Castro, B. Latorre,J. Vela-Tambo, J. Gracia

Environmental Modelling & Software

TLDR: SeqIA is a Python framework that uses advanced NLP to extract and classify drought impacts from Spanish news articles, providing structured data with locations and dates to support drought impact analysis.

Drought, Journal, Large Language Models, Natural Language Processing

Distributed Simulation with Efficient Fault Tolerance

J. Vela-Tambo, U. Arronategui, J.A. Bañares, J.M. Colom

International Conference on the Economics of Grids, Clouds, Systems, and Services (GECON 2024)

TLDR: Presents a low-latency, replication-based fault tolerance method for conservative distributed simulation. The approach reduces messaging and synchronization, maintains consistency, and achieves near-native performance with minimal overhead and fast recovery after faults.

Conference, Distributed Simulation, Fault Tolerance

Design and Implementation of Fault Tolerance with Low Latency in Distributed Simulation

J. Vela-Tambo, U. Arronategui, J.A. Bañares

TLDR: Undergraduate Thesis for Computer Engineering Degree at the University of Zaragoza. A distributed simulator for discrete event systems featuring an innovative fault tolerance model based on decoupled replication, achieving optimal performance in fault-free scenarios while ensuring robust recovery and scalability in the presence of faults.

Distributed Simulation, Fault Tolerance, Undergraduate Thesis

Cross-lingual ontology matching with CIDER-LM: results for OAEI 2022

J. Vela-Tambo, J. Gracia

The 17th International Workshop on Ontology Matching collocated with the 21th International Semantic Web Conference ISWC-2022

TLDR: In this paper, the CIDER-LM cross-lingual ontology matching system is presented, as well as the results it achieved during the OAEI (Ontology Alignment Evaluation Initiative) 2022 campaign.

Conference, Large Language Models, Natural Language Processing, Ontology Matching