QuantumX: an experience for the consolidation of Quantum Computing and Quantum Software Engineering as an emerging discipline
Juan M. Murillo, Ignacio Garc\'ia Rodr\'iguez de Guzm\'an, Enrique Moguel, Javier Romero-\'Alvarez, Jaime Alvarado-Valiente, \'Alvaro M. Aparicio-Morales, Jose Garcia-Alonso, Ana D\'iaz Mu\~noz, Eduardo Fern\'andez-Medina, Francisco Chicano, Carlos Canal, Jos\'e Daniel Viqueira

TL;DR
QuantumX is a pioneering conference that brought together Spanish research on quantum computing and software engineering, fostering collaboration and outlining future challenges in this emerging interdisciplinary field.
Contribution
This paper reports on the inaugural QuantumX track, highlighting its role in establishing a research community and identifying key themes and open challenges in quantum software engineering.
Findings
Established a collaborative research community in Spain and Ibero-America.
Identified key research themes and engineering concerns in quantum software.
Outlined open challenges and future directions for the field.
Abstract
The first edition of the QuantumX track, held within the XXIX Jornadas de Ingenier\'ia del Software y Bases de Datos (JISBD 2025), brought together leading Spanish research groups working at the intersection of Quantum Computing and Software Engineering. The event served as a pioneering forum to explore how principles of software quality, governance, testing, orchestration, and abstraction can be adapted to the quantum paradigm. The presented works spanned diverse areas (from quantum service engineering and hybrid architectures to quality models, circuit optimization, and quantum machine learning), reflecting the interdisciplinary nature and growing maturity of Quantum Computing and Quantum Software Engineering. The track also fostered community building and collaboration through the presentation of national and Ibero-American research networks such as RIPAISC and QSpain, and through…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Cloud Computing and Resource Management · Scientific Computing and Data Management
