Methods and technologies of data processing in heterogeneous computing environments

• Vladimir Korenkov (JINR)

Room: Ramses BallRoom

Educational programs of the Joint Institute for Nuclear Research

• Dmitry Kamanin (Joint Institute for Nuclear Research)

Room: Ramses BallRoom

Entanglement and Wigner Function Negativity of Qubits as Quantum Resources

• Arsen Khvedelidze (JINR)

Room: Ramses BallRoom A crucial goal of quantum technologies is to utilise deviations in properties of quantum system from their classical counterparts as a resource that allows to significantly improve the effectiveness of classical devices. Among such prominent properties of quantum systems are entanglement of states and attainability of negative values of quasiprobability distributions. In the report, bearing in mind this goal, we present description of resourceful states of finite-dimensional quantum system as complements to the convex sets of separable states and states whose Wigner functions are nonnegative. Several computational aspects of the resource quantification will be exemplified for system of qubits.

Studying Nonequilibrium Dynamics: Quantum Models and Algorithms

• Victor Yushankhai (JINR)

Room: Ramses BallRoom to be done

MACHINE LEARNING-ASSISTED FTIR ANALYSIS OF HEPATOCELLULAR CARCINOMA

• MAHMOUD DARWICH (OHIO, USA)
• MAY M.EID (NATIONAL RESEARCH CENTER EGYPT)

Room: Ramses BallRoom

Exploring the potential of D-Wave Advantage 2 Quantum Annealer for Particle Tracking

• Martin Bures (JINR)

Room: Ramses BallRoom

ML/DL/HPC ecosystem of the "Govorun" supercomputer

• Maxim Zuev (JINR)

Room: Ramses BallRoom to be done

On Bernstein Paradox In Multy-Qubit Systems

• Vahagn Abgaryan (JINR MLIT)

Room: Ramses BallRoom

Hybrid Quantum–VLC Systems: A Step Towards Secure and Power-Efficient Wireless Communication

• Sara Ahmed (Faculty of Engineering)

Room: Ramses BallRoom Quantum technology is opening new horizons in next-generation wireless communication, with Visible Light Communication (VLC) emerging as a strong candidate to complement and extend traditional systems. While VLC offers high data rates and efficient spectrum utilization, it faces persistent challenges, including security limitations, high Peak-to-Average Power Ratio (PAPR), and LED nonlinearity. In this work, we propose a Hybrid Quantum–VLC (HQVLC) system that integrates quantum communication principles with the Asymmetrically Clipped Optical OFDM (ASCO-OFDM) modulation scheme. This approach ensures not only secure and robust data transmission, but also high-rate and spectrum-efficient communication. To further optimize performance, we incorporate companding and precoding techniques to mitigate the effects of PAPR and LED nonlinearity, thereby achieving a more power-efficient system. The result is a VLC framework that delivers both quantum-enhanced security and optimized power efficiency—a dual advantage that positions HQVLC as a promising step towards the future of secure and high-performance wireless communication systems.

Methods and Algorithms for HEP Data Processing

• Sergei Shmatov (JINR)

Room: Ramses BallRoom

Quantum Machine Learning: Mathematical Foundations and Real-World Applications

• Abeer Aly (Higher canal institute for engineering and technology)

Room: Ramses BallRoom The rapid progress in quantum information science has opened promising directions for solving complex computational problems in ways unattainable by classical methods. Among these, quantum machine learning (QML) offers a powerful framework that merges quantum algorithms with data-driven approaches, enabling potential speedups in classification, optimization, and simulation tasks. This presentation focuses on the mathematical principles underlying QML, including the formulation of quantum circuits for learning models, representation of data in high-dimensional Hilbert spaces, and analysis of computational complexity. Examples will be drawn from both theoretical and applied contexts, illustrating how hybrid quantum–classical methods can address challenges in science and technology. The discussion will highlight current research trends, open problems, and possible paths toward practical implementation on near-term quantum devices.

Graph neural network with an attention mechanism for clustering particle tracks by events in the SPD experiment

• Savelii Omelyanchuk (JINR)

Room: Ramses BallRoom This work is dedicated to the development of deep learning methods for particle track classification. It focuses on the graph neural network (GNN) architecture for classifying tracks by events in each time slice at the SPD experiment. The work presents a novel approach to track sorting, an analysis of training dynamics, and model testing under various conditions. The model is implemented and trained using modern deep machine learning tools that enable parallel tensor computations.

Machine Learning Algorithms for Water Hyacinth Monitoring

• Mahmoud Saleh (Channel Maintenance Research Institute (CMRI), National Water Research Center (NWRC))

Room: Ramses BallRoom Machine learning algorithms represent a transformative advance in monitoring water hyacinth and aquatic vegetation using satellite imagery, particularly in Egypt and Africa, where invasive aquatic weeds pose significant ecological and economic challenges. Recent studies illustrate that RF, SVM, and CNN methods applied to Sentinel-2 and Landsat data improve detection accuracy, temporal monitoring, and assessment of management interventions. Continued development integrating multi-sensor data, explainable approaches, and enhanced training datasets will further boost capability, supporting sustainable water resource management in vulnerable regions.

Progressive Hybrid Quantum-Classical Generative Adversarial Network for Image Generation

• Nikita Ryabov (JINR)

Room: Ramses BallRoom

Using machine learning models in aviation and energy production

• Youssef Tohamy (Aeronautical Engineering Technology Student (Scientific Researcher))

Room: Ramses BallRoom Machine learning (ML) is emerging as a critical enabler of innovation in both unmanned aerial systems (drones) and energy production. In the field of drones, ML models provide advanced capabilities that significantly improve autonomy, safety, and efficiency. Through computer vision and deep learning, drones can perform real-time object detection, terrain recognition, and collision avoidance, enabling them to operate in complex and dynamic environments with minimal human intervention. Predictive analytics powered by ML also enhances UAV maintenance by processing sensor data from engines, batteries, and structural components to anticipate faults before they occur, thus extending operational reliability. A practical example of this is a drone project developed to recognize and classify land surfaces. By using ML-based image recognition, the drone can automatically detect illegal land encroachments, construction on restricted areas, and changes in residential surroundings. This application has proven to be highly effective for urban planning, environmental monitoring, and law enforcement, providing decision-makers with accurate, real-time insights. In energy production, ML contributes to optimizing both renewable and conventional systems. Drones integrated with ML-powered vision systems are increasingly deployed to inspect wind turbines, solar farms, and oil pipelines, reducing manual inspection costs, improving safety, and accelerating fault detection. Beyond inspections, ML is also used to forecast renewable energy output by analyzing weather conditions, energy demand, and performance data. For example, wind energy projects use ML to predict turbine performance and optimize blade alignment, while solar energy systems apply ML to forecast solar radiation and detect defects in panels. Large-scale collaborations, such as *Google DeepMind’s AI for grid management*, demonstrate how ML can balance supply and demand, making renewable energy more reliable and scalable. By bridging drones and energy production, ML not only transforms how industries manage efficiency and safety but also advances global goals of sustainability and innovation. The integration of ML into these two critical sectors illustrates its potential to redefine industrial operations, reduce risks, and accelerate the transition towards smarter and cleaner technologies.

Development of hybrid machine learning pipelines using PennyLane quantum templates

• Mikhail Katulin (JINR)

Room: Ramses BallRoom

Quantum-AI Convergence for Human-Centric Innovation: Education, Ethics, and Empowermen

• Wael Badawy

Room: Ramses BallRoom

On the Mitigability of Probabilistic Photonics CNOT Failures of Variational Quantum Classifiers

• Ahmed ElMahdy

Room: Ramses BallRoom