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Giuseppe Serra is an Associate Professor at University of Udine, Italy, since November 2019. He received his Ph.D. in Computer Engineering, Multimedia and Telecommunications in 2010 at the University of Florence, Italy. From 2014 to 2016 he was an Assistant Professor at the University of Modena and Reggio Emilia, Italy. He was a visiting researcher at Carnegie Mellon University, Pittsburgh, USA, and at Telecom ParisTech/ENST, Paris, in 2006 and 2010 respectively. His research interests include Machine Learning and Deep Learning. He was the lead organizer of the “International Workshop on Egocentric Perception, Interaction and Computing (EPIC)” in 2016 and 2017 (ECCV’16– ICCV’17) and he gave tutorials at two international conferences (ICPR’12, CAIP’13). He also serves as an Editor Board of IEEE THMS and ACM TOMM. He was a Technical Program Committee member of several conferences and workshops. He regularly serves as reviewer for international conferences and journals such as AAAI, ICML, NIPS, ACL, ECCV, CVPR, IEEE TPAMI, IEEE TMM. He has published more than 70 publications in the most prestigious journals and conferences in the field. He supervised more than 10 PhD Students. He is leading the Artificial Intelligence Laboratory of Udine.

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Daniele Lizzio Bosco is currently a third-year PhD candidate in the inter-university doctoral program jointly hosted by the University of Udine and the University of Naples Federico II, where he conducts research within the AI Laboratory (AI LAB) under the supervision of Professor Giuseppe Serra. His work sits at the intersection of quantum computing and artificial intelligence, with a primary focus on Quantum Artificial Intelligence and the development of hybrid deep learning and quantum computing methods to enhance computational performance and algorithmic expressivity. Beyond his research outputs, Daniele is actively involved in the scientific community. He serves on the local organising committee of the European Summer School on Quantum AI (EQAI) and participates in program and reviewing committees for international workshops and journals.

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Jacopo Cossio is a researcher working at the intersection of Mathematics, Artificial Intelligence, and Quantum Computing. He holds a Bachelor’s degree in Mathematics from the University of Udine, where his thesis explored the applications of tropical geometry and the Riemann-Roch theorem. He subsequently earned a Double Master’s Degree in Artificial Intelligence & Cybersecurity from the University of Udine (Italy) and Alpen-Adria-Universitaet Klagenfurt (Austria), graduating with honors (110/110 cum laude). His research focuses on optimizing quantum circuits through advanced Machine Learning techniques. In his master’s thesis and subsequent publications, he addressed the CNOT Synthesis Problem, introducing a novel Reinforcement Learning (RL) approach to minimize gate counts. He is a co-organizer of the 5th edition of the European Quantum Artificial Intelligence (EQAI 2026), a summer school held in Lignano Sabbiadoro, IT. His recent research was featured at the 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI).

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Carla Piazza is a Full Professor of Computer Science at the Department of Mathematics, Computer Science, and Physics of the University of Udine, where she also coordinates the Computational Biology and Bioinformatics Research Group. She received her Master’s degree in Mathematics from the University of Parma (cum laude) and her Ph.D. in Computer Science from the University of Udine in 2002. Her diverse research interests span Systems Biology, Hybrid Automata, Model Checking, Information Flow Security, and Quantum Computing. She has authored over 100 scientific publications and serves as the Coordinator of the Bachelor and Master degrees in Computer Science at the University of Udine. Throughout her career, she has also been a visiting researcher at the Courant Institute of New York University on multiple occasions.

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Marco Cerezo obtained his undergrad and PhD from the National University of la Plata. He is currently a Staff Scientist in Information Sciences (CAI-3) at Los Alamos National Laboratory and a member of the Quantum Science Center, with research spanning quantum computing, quantum information, quantum algorithms, and quantum machine learning. His work has made influential contributions to variational quantum algorithms, quantum neural networks, and the theory of trainability in parameterized quantum systems, and he has also recently contributed to AI-driven quantum circuit optimization. He has authored more than 65 publications, with nearly 18,000 citations according to Google Scholar, and has published in leading venues including Nature Reviews Physics, Nature Computational Science, Nature Communications, Nature Physics, and PRX Quantum. In addition to his research, he is deeply engaged in community building and education: he is a principal organizer of the Quantum Computing Summer School at Los Alamos, has delivered invited lectures and seminars internationally, and has extensive mentoring experience with students, postdocs, and early-career researchers.

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Lukasz Cincio is a staff scientist at Los Alamos National Laboratory whose research spans quantum science and computational physics. He earned his PhD from Jagiellonian University in Poland, completed a postdoctoral appointment at the Perimeter Institute for Theoretical Physics, and later was an Oppenheimer Fellow at LANL. He has published around 100 papers, with more than 18,000 citations and an H-index of 52 (Google Scholar), including work in leading journals such as Nature Reviews Physics, Nature Communications, Nature Computational Science, and Physical Review Letters. In addition to his research, he has mentored postdocs and students, led multiple DOE and internal projects, and co-organized the Quantum Computing Summer School.

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Lirandë Pira is a computer scientist with a PhD in Quantum Algorithms and Machine Learning from the University of Technology Sydney, Australia. She is currently a Research Fellow at the Centre for Quantum Technologies in Singapore, where she studies how quantum systems learn, developing theoretical foundations at the intersection of quantum physics, machine learning, and computer science. Her research spans trainable near-term quantum models, fault-tolerant methods based on quantum linear algebra, and learning theory, with a focus on what works in practice. She is actively involved in the quantum research community, serving as co-chair of the Quantum Techniques in Machine Learning (QTML) 2025 conference and on the organizing committee for Machine Learning for Quantum Technologies Symposium (ML4QT) 2026, and Quantum Information Processing (QIP) 2027, as well as contributing to editorial and program committee activities.

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Elié Gouzien is a Lead Scientist for Quantum Algorithms at Alice & Bob in Paris, France. Following his post-doctoral research at the Institut de Physique Théorique (IPhT) in the Sangouard Team, his current work focuses on fault-tolerant quantum computing architectures and the development of quantum algorithms using superconducting Schrödinger cat qubits. He is particularly recognized for his research on the end-to-end resource estimation of complex algorithms over distributed and modular architectures, including notable publications on the resources required for factoring RSA integers and computing Elliptic Curve Logarithms. His work frequently bridges the gap between quantum optics, theoretical computer science, and practical quantum circuit synthesis, with high-impact publications in venues such as Physical Review Letters and Nature Communications. He is actively engaged in the broader quantum community, serving as an invited speaker at international events such as the Quantum Computing Summer School at Los Alamos National Laboratory.

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Dr. Christa Zoufal manages the IBM Quantum UK Research team that works in close collaboration with national labs and university to develop impactful quantum algorithms & applications. Furthermore, she is a Quantum Applications Researcher at IBM Research-Europe, Zurich, where she focuses on developing and evaluating quantum algorithms for practical applications in optimization, machine learning, and simulation. She earned her Ph.D. at ETH Zürich in 2021, focusing on Generative Quantum Machine Learning and its scalability. Her current work bridges the gap between theoretical quantum computing and real-world use cases, contributing to the advancement of quantum technologies within industry and academia. Christa was also actively involved with the Unitary Fund from 2020-2025 , supporting open-source quantum software and community-driven innovation. Her research aims to identify quantum advantage in meaningful contexts and accelerate the integration of quantum computing into existing computational workflows by bridging academia, industry, and public engagement to drive quantum impact.