Uman Khalid

The Metaverse is a connected virtual space that has recently emerged as a new frontier for human interaction and experiences. For ideal immersive Metaverse experiences, perpetual data traffic poses stringent requirements on computation latency, communication bandwidth, data privacy, and transmission delay. Semantic communication with machine learning provides a nascent paradigm shift from context-agnostic communication toward semantics-centric communication to address these underlying challenges

The quantum internet is envisaged to connect quantum systems at a global scale to provide diverse applications unimaginable with the classical internet. However, the technological shortcomings and practical challenges of noisy intermediate-scale quantum (NISQ) devices impose several constraints on the vision of a truly global quantum internet. Recent experimental developments for quantum networks are laying the pathway from small-scale to intermediate-scale noisy quantum networks dedicated to sp

Measurement-based quantum correlations (MbQCs) depend on how strongly an observer perturbs the unobserved system. This distinctive property differentiates MbQCs from traditional quantum correlations such as entanglement and discord. We utilize MbQCs to elucidate quantum information processing capabilities in quantum computation and quantum state discrimination. We show that MbQCs exist more generally than entanglement and discord in optimal assisted quantum state discrimination and in a determin

Abstract In traditional quantum metrology protocols, the initial multipartite entangled pure quantum probes are considered to be isolated, i.e., free of quantum many-body effects. Here, we study the impact of inherent many-body effects such as interaction with noisy environment and nonlocal interactions among particles on metrologically resourceful multipartite entanglement of initially mixed quantum probes. In this regard, we employ an information-theoretic multipartite entanglement measure as

Quantum sensing networks (QSNs) embody a fusion of quantum sensing and quantum communication, achieving both Heisenberg precision and unconditional security through the exploitation of quantum phenomena such as superposition and entanglement. However, the scalability and sensing capability of QSNs in the noisy intermediate-scale quantum (NISQ) era face challenges due to technological constraints, device imperfections, quantum noise, and dynamic sensing scenarios. Recent advancements in integrati

The learning of fundamental quantum properties—namely coherence, discord, and entanglement—benchmarks the security, computational, and metrological capability of noisy intermediate-scale quantum (NISQ) communication, computing, and sensing networks. The current learning techniques vary widely for these fundamental quantum properties, including standard tomographic procedures that involve exhaustive optimization. Fortunately, the fundamentally distinct quantum properties feature an intricate conn

Satellite imagery plays a crucial role in integrated satellite-ground remote sensing (SGRS), particularly in applications such as disaster management and military intelligence, where real-time monitoring and forecasting are essential for effective decision-making. However, narrow artificial intelligence (AI) models often face challenges in processing large-scale high-dimensional data efficiently while maintaining the required accuracy and speed, limiting their effectiveness in time-sensitive sce

Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well s

The detection of fundamental quantum resources-namely coherence, discord, and entanglement-benchmarks the metrological power of quantum sensing networks. Traditional methods for certifying these resources, like exhaustive optimization-based tomographic procedures, are resource-intensive and vary significantly. This paper proposes a framework for identifying metrologically useful quantum sensing probes by detecting fundamental quantum resources. Herein, we introduce a witness-based certification

Quantum sensing networks (QSNs) are expected to play a critical role in quantum networks by achieving measurement precision unattainable with classical methods, leveraging quantum properties such as superposition and entanglement. Distributed quantum sensing, a key application of QSNs, can reach Heisenberg-limited precision scaling with the number of sensors involved. However, practical implementation faces significant challenges due to noise effects, complicating the optimal selection of sensor