Prof. Dr. Gabriel Gustavo Carlo | Quantum Science | Best Researcher Award

Prof. Dr. Gabriel Gustavo Carlo | Quantum Science | Best Researcher Award

Prof. Dr. Gabriel Gustavo Carlo | Researcher | National Scientific and Technical Research Council | Argentina

Academic Background

Gabriel Gustavo Carlo holds a Doctorate in Physics and a Licentiate in Physics from the University of Buenos Aires. His academic journey reflects a deep commitment to both theoretical and applied aspects of quantum mechanics and complex systems. According to Scopus, he has contributed to over 50 documents, which have been cited by more than 500 works, reflecting his significant presence in the scientific community. His h-index on Scopus stands at eighteen, demonstrating consistent scholarly impact, while his Google Scholar profile reports over 700 citations, a cumulative h-index of 20, and an i10-index of 35, underscoring his influence and visibility in the field.

Research Focus

Carlo’s research centers on quantum chaos, dissipative systems, and the interplay between classical and quantum dynamics. He explores quantum computation, quantum reservoir computing, and the role of noise in enhancing quantum algorithms. His work aims to bridge foundational theory with practical computational applications, emphasizing the optimization of quantum circuits and the understanding of complex behaviors in open quantum systems.

Work Experience

He currently serves as an independent researcher at the National Scientific and Technical Research Council (CONICET), associated with the TANDAR Laboratory in Buenos Aires. Carlo has held teaching positions at multiple institutions, including the University of Buenos Aires, National University of Quilmes, Favaloro University, and the National University of General San Martín, where he continues to guide and mentor graduate and undergraduate students. His career reflects a combination of research leadership, project management, and pedagogy.

Key Contributions

Carlo has directed numerous doctoral and undergraduate theses, fostering the development of new talent in the fields of quantum physics and complex systems. His studies have introduced methods for analyzing quantum dissipative systems, optimal parameterized quantum circuits, and the use of machine learning to explore quantum localization. He has significantly contributed to understanding the correspondence between classical and quantum dynamics and the role of environmental effects in quantum systems.

Awards & Recognition

He has been recognized as an outstanding referee for the Physical Review journals, reflecting his expertise and reputation in evaluating cutting-edge research in physics.

Professional Roles & Memberships

Carlo actively participates in the international scientific community as a referee for leading journals including Physical Review, Physical Review Letters, Scientific Reports, and Chaos. He serves on editorial and reviewer boards for journals such as Entropy and contributes to Mathematical Reviews. Additionally, he has organized international workshops and seminars on quantum chaos, fostering collaboration across institutions in Europe and Latin America.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Bergamasco, P. D., Carlo, G. G., & Rivas, A. M. F. Spectral truncation of out-of-time-ordered correlators in dissipative systems. Physical Review E, 112, 034201.

Rivas, A. M. F., Vergini, E. G., Ermann, L., & Carlo, G. G. Ideal gas law for a quantum particle. Physical Review E, 112, 014223.

Montes, J., Borondo, F., & Carlo, G. G. Optimal multicore quantum computing with few interconnects. APL Quantum, 2, 026104.

Domingo, L., Grande, M., Carlo, G., Borondo, F., & Borondo, J. Optimal quantum reservoir computing for market forecasting. arXiv:2401.03347.

Correr, G. I., Azado, P. C., Soares-Pinto, D. O., & Carlo, G. Optimal complexity of parameterized quantum circuits. arXiv:2405.19537.

Impact Statement / Vision

Carlo envisions advancing the field of quantum technologies by integrating foundational physics with practical computational strategies. His work aims to optimize quantum computation under realistic conditions and provide frameworks for understanding complex quantum systems, bridging theory and application to address future challenges in science and technology.

Mr. Muhammad Waqas Arshad | Quantum Computing | Best Researcher Award

Mr. Muhammad Waqas Arshad | Quantum Computing | Best Researcher Award

PhD Researcher, University of Bologna, Italy

Muhammad Waqas Arshad is a passionate Research Scholar specializing in Artificial Intelligence, Quantum Computing, and Data Science 🌍💡. With a strong analytical mindset and a drive for innovation, he focuses on developing computational solutions for real-world challenges, particularly in intelligent transportation systems and automotive engineering 🚗🔬. He actively collaborates on interdisciplinary projects, bridging theoretical research with practical applications. As a researcher with a futuristic vision, he is dedicated to pushing the boundaries of emerging technologies and contributing to industrial advancements. Currently, he is a Visiting Scholar at Purdue University Fort Wayne, USA 🎓🇺🇸.

Publication Profile

Education 🎓

Muhammad Waqas Arshad has a rich academic background in Computer Science and Artificial Intelligence. He is currently pursuing a Ph.D. in Computer Science and Engineering at the University of Bologna, Italy 🇮🇹 (2022-Present) 🏛️. He holds an MS in Artificial Intelligence (Creative Technologies) from Air University, Islamabad, Pakistan 🇵🇰 (2019-2022) 📚. His journey in computer science began with a BS-IT from Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan (2015-2019) 🎓. His multidisciplinary expertise enables him to tackle complex research problems with a strong computational foundation.

Experience 💼

With extensive research experience, Muhammad Waqas Arshad is currently a Visiting Scholar in Computer Science at Purdue University Fort Wayne, USA 🌎💻. He has been actively involved in advanced AI applications and quantum computing methodologies, focusing on intelligent optimization techniques for automotive systems and energy forecasting 🔬🚀. His work spans various international collaborations, contributing to scientific advancements in multi-objective optimization, AI-driven decision-making, and machine learning algorithms 🤖📊.

Awards and Honors 🏅

Muhammad Waqas Arshad has received several academic and professional accolades for his contributions to research and technology 🌟. His innovative research publications and active participation in AI and quantum computing conferences have earned him recognition in the global research community 🏆. As a dedicated IEEE member and General Secretary at ISOC-BSIG, he has made significant contributions to scientific communities and AI-driven technological advancements 📜.

Research Focus 🔬

His research interests primarily revolve around Artificial Intelligence, Quantum Computing, and Data Science 🧠💡. He specializes in AI-driven automotive engineering, intelligent transportation systems, and energy-efficient solutions for electric vehicle charging stations ⚡🚘. Additionally, he explores multi-objective optimization using AI and quantum approaches, aiming to enhance real-world decision-making processes 🏗️🔎. His work integrates machine learning, deep learning, and computational intelligence to drive scientific and industrial breakthroughs 🚀📈.

Conclusion 🌟

Muhammad Waqas Arshad is a visionary researcher committed to bridging the gap between theoretical research and industrial innovation 🚀🎯. His pioneering work in AI, quantum computing, and data science is shaping the future of intelligent systems and automation 🌍🤖. With a strong academic background, international research experience, and a passion for technology, he is actively contributing to the next generation of computational advancements 📚⚡.

Publications 📚🔗

Multi-Objective Optimization of Independent Automotive Suspension by AI and Quantum Approaches: A Systematic Review (2025) – Published in Machines 🏎️ 🔗DOI: 10.3390/machines13030204

Optimization of Double Wishbone Suspension: Evaluating the Performance of Classes of Algorithms (2024) – ICAMCS Conference 🔬 🔗DOI: 10.1109/icamcs62774.2024.00025

AI-Driven Energy Forecasting for Electric Vehicle Charging Stations Powered by Solar and Wind Energy (2024) – Smart Grid Conference🔗DOI: 10.1109/icsmartgrid61824.2024.10578078

Comparative Analysis of MOORA and GRA with PIPRECIA Weighting in Warehouse Head Selection (2024) – BEES Journal 📦 🔗DOI: 10.47065/bees.v4i3.4922

Combination of Multi-Attributive Ideal-Real Comparative Analysis and Rank Order Centroid in Supplier Performance Evaluation (2024) – KLIK Journal 📊

Detection of Human Gender from Eye Images Using DNN Approach (2023) – Teknoinfo Journal 👁️

Role of AI in Decision Making and Its Socio-Psycho Impact on Jobs, Project Management, and Business of Employees (2023) – Journal for ReAttach Therapy 🏢

Applications of Quantum Computing in the Health Sector (2023) – Journal of Data Science and Intelligent Systems 🏥 🔗DOI: 10.47852/bonviewJDSIS3202656

Prediction and Diagnosis of Breast Cancer Using Machine Learning and Ensemble Classifiers (2023) – Open Science Framework 🎗️ 🔗DOI: 10.17605/OSF.IO/9CFN6

Advanced Face Detection Using Machine Learning and AI-Based Algorithm (2022) – IC3I Conference 🤖 🔗DOI: 10.1109/ic3i56241.2022.10072527