Global Particle Physics Excellence Awards

Introduction The field of quantum control has seen remarkable advancements in recent years, particularly in addressing the challenges of managing large ensembles of quantum systems with varying internal parameters. In this research, we introduce a novel probabilistic control framework that enables efficient steering of an ensemble of quantum systems while compensating for external environmental interactions. By targeting the probabilistic description of quantum dynamics rather than deterministic trajectories, the proposed approach provides enhanced robustness and adaptability, opening new pathways for precise quantum ensemble manipulation. Research Motivation Controlling quantum systems at the ensemble level is inherently challenging due to parameter variations, environmental influences, and stochastic nature of quantum processes. Traditional control methods often struggle to maintain accuracy across diverse system configurations. This research is motivated by the need for a unified ...

Introduction Wireless network coverage optimization plays a pivotal role in enhancing service quality and ensuring seamless connectivity in modern communication infrastructures. However, as networks grow in scale and complexity, traditional optimization methods face computational bottlenecks. The advent of quantum computing offers promising alternatives, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era, where hybrid approaches can be leveraged for efficiency. This work introduces a quantum-driven divide-and-conquer method that models coverage as a graph problem, partitions it via QUBO formulation, and employs advanced quantum algorithms to deliver scalable solutions. Quantum Modelling of Network Coverage The research models the wireless coverage optimization problem as a covering graph, enabling a structured representation of network nodes, coverage zones, and overlap constraints. This graph-based model provides a natural mapping to Quadratic Unconstrained Binary Optimiz...

  Introduction Atmospheric aerosols significantly influence climate, air quality, and human health, yet their vertical distribution and physicochemical properties remain underexplored. Traditional ground-based observations often fail to capture altitude-dependent variations in aerosol composition and mixing states. To address this gap, a novel drone-based aerosol sampling system integrated with a rotating cascade impactor has been developed. This innovation enables size-selective and altitude-resolved single-particle sampling, combined with advanced Raman-based spectroscopic analyses, providing new insights into particle transformations and aging processes across different atmospheric layers. Development of the Drone-Based Rotating Cascade Impactor System The newly engineered drone-mounted rotating cascade impactor features a series of rotating impaction stages designed for precise size-selective sampling of atmospheric particles. Its lightweight and portable design ensures oper...

Introduction Soft magnetic materials have emerged as a vital class of materials for sensor applications, especially in structural health monitoring. Their adaptability for integration as microwires within composite structures offers exceptional opportunities for real-time evaluation of stress, deformation, and other structural parameters. Extensive research demonstrates their effectiveness across multiple industries due to their sensitivity to electromagnetic parameters. This study focuses on analyzing the influence of the relative sample position on measurements, addressing the challenge of modeling due to experimental variability. Soft Magnetic Materials in Sensing Applications Soft magnetic materials, particularly in microwire form, offer unique advantages such as high sensitivity to stress and excellent compatibility with composite structures. Their ability to operate effectively under varying industrial conditions has made them a preferred choice for structural monitoring solution...

Introduction Quantum computing is on the cusp of transforming computational capabilities across industries, yet its advancement is hindered by limitations in qubit scalability on individual quantum processors. Distributed Quantum Computing (DQC), which connects multiple Quantum Processor Units (QPUs) via quantum networks, presents a promising approach to overcoming this bottleneck. However, this paradigm shift introduces new layers of complexity, especially in the translation of high-level quantum algorithms to executable low-level code across distributed systems. Existing intermediate representations lack the necessary abstractions for such environments. To address this gap, this paper introduces NetQIR—an extension of Microsoft’s Quantum Intermediate Representation (QIR)—which integrates networking capabilities into quantum programming frameworks, enabling modular and scalable quantum software infrastructures for distributed execution. The Role of Intermediate Representations in Dist...