arXiv subjects

Saif Al-Kuwari

Publications and source records attributed to Saif Al-Kuwari.

At least 73 records · Page 4Linked to original sources

arXiv2025

Coherence, Transport, and Chaos in 1D Bose-Hubbard Model: Disorder vs. Stark Potential

Quantum coherence and phase transitions are studied in a finite one-dimensional Bose--Hubbard model using exact diagonalization under thermal fluctuations, a Stark potential, and disorder. The condensate fraction, superfluid fraction, visibility, number fluctuations, and the $\ell_1$-norm of coherence are computed to characterize the Mott insulator--superfluid transition. Although finite-size effects prevent a sharp transition, ground-state properties reveal signatures of quantum criticality. Thermal fluctuations can enhance coherence via tunneling, a Stark potential promotes localization, and disorder suppresses global superfluidity while preserving local coherence. These results highlight how disorder, tilt, and temperature reshape coherence and offer insights for quantum simulation and strongly correlated phases. For systems up to six sites with unit filling, a spectral analysis is also performed through the metric mean gap ratio (MGR). However, limited statistics due to the small system size and computational constraints prevent a complete characterization of quantum chaos, yielding only approximate signatures.

cond-mat.quant-gas↗

arXiv2025

Magnetic Dipolar Quantum Battery with Spin-Orbit Coupling

We investigate a magnetic dipolar system influenced by the $z$-component of Zeeman splitting, Dzyaloshinsky--Moriya (DM) interaction, and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) exchange interaction, with emphasis on the role of quantum resources in both closed and open settings. By analyzing the Gibbs thermal state and solving the Lindblad master equation, we study the behavior of quantum coherence, discord, and entanglement under thermal equilibrium and dephasing noise. After exploring these resources, we apply the model to a closed quantum battery (QB). Our results show that while Zeeman splitting degrades quantum resources in noisy and thermal regimes, it enhances QB performance by improving ergotropy, anti-ergotropy, storage capacity, and coherence during cyclic charging. The axial parameter further amplifies performance, leading to coherence saturation and persistent ergotropy growth, in line with the notion of incoherent ergotropy. KSEA interaction and the rhombic term consistently preserve coherence and entanglement under noise, thereby strengthening QB functionality. DM interaction mitigates thermal degradation of resources in the Gibbs state and improves performance, though its effect is limited under Pauli-$X$ dephasing. We reveal diverse behaviors, including increased ergotropy without coherence and the coexistence of coherence with zero extractable work. Finally, we propose Nuclear Magnetic Resonance (NMR) as a feasible platform for experimental implementation.

quant-ph↗

arXiv2025

Quantum thermometric sensing: Local vs. Remote approaches

Quantum thermometry leveraging quantum sensors is investigated with an emphasis on fundamental precision bounds derived from quantum estimation theory. The proposed sensing platform consists of two dissimilar qubits coupled via capacitor, which induce quantum oscillations in the presence of a thermal environment. Thermal equilibrium states are modeled using the Gibbs distribution. The precision limits are assessed through the Quantum Fisher Information (QFI) and the Hilbert-Schmidt Speed (HSS), serving as stringent criteria for sensor sensitivity. Systematic analysis of the dependence of QFI and HSS on tunable parameters -such as qubit energies and coupling strengths- provides optimization pathways for maximizing temperature sensitivity. Furthermore, we explore two distinct quantum thermometry paradigms: (I) local temperature estimation directly performed by Alice, who possesses the quantum sensor interfacing with the thermal bath, and (II) remote temperature estimation conducted by Bob, facilitated via quantum teleportation. In the latter scenario, temperature information encoded in the qubit state is transmitted through a single-qubit quantum thermal teleportation protocol. Our findings indicate that direct measurement yields superior sensitivity compared to remote estimation, primarily due to the inherent advantage of direct sensor-environment interaction. The analysis reveals that increasing Josephson energies diminishes sensor sensitivity, whereas augmenting the mutual coupling strength between the qubits enhances it.

quant-ph↗

arXiv2025

Quantum Reinforcement Learning: Recent Advances and Future Directions

As quantum machine learning continues to evolve, reinforcement learning stands out as a particularly promising yet underexplored frontier. In this survey, we investigate the recent advances in QRL to assess its potential in various applications. While QRL has generally received less attention than other quantum machine learning approaches, recent research reveals its distinct advantages and transversal applicability in both quantum and classical domains. We present a comprehensive analysis of the QRL framework, including its algorithms, architectures, and supporting SDK, as well as its applications in diverse fields. Additionally, we discuss the challenges and opportunities that QRL can unfold, highlighting promising use cases that may drive innovation in quantum-inspired reinforcement learning and catalyze its adoption in various interdisciplinary contexts.

quant-ph↗

arXiv2025

Quantum speed limit as a sensitive probe of Planck-scale effects

Many quantum-gravity scenarios predict a minute modification of the canonical commutator, known as the generalized uncertainty principle (GUP), whose low-energy signatures are, in principle, accessible to state-of-the-art laboratory tests. We compute first-order minimal-length corrections to the quantum speed limit (QSL) for three cases: uniform superpositions in an infinite square well, coherent harmonic-oscillator states, and squeezed-oscillator states. We identify a universal amplification law: for any pure state, the fractional shift of either speed limit scales linearly with $β$ and algebraically with the state's effective Hilbert-space size. As the effective Hilbert-space dimension can be exceedingly large, the associated minimal-length signatures are amplified by several orders of magnitude. Using high-precision matter-wave timing data, we set a direct bound on the GUP parameter $β$, which quantifies minimal-length quantum-gravity effects. Our analysis indicates that phase-locked, short-time overlap fits on kilogram-scale optical-spring modes can tighten this bound by orders of magnitude. We outline two implementable measurement pipelines -- continuous back-action-evading single-quadrature readout and stroboscopic, phase-locked pulsed tomography -- that exploit this leverage, making QSL-based timing a practical, near-term probe of minimal-length physics on quantum-optical and optomechanical platforms.

gr-qc↗

arXiv2025

Collaborative Filtering using Variational Quantum Hopfield Associative Memory

Quantum computing, with its ability to do exponentially faster computation compared to classical systems, has found novel applications in various fields such as machine learning and recommendation systems. Quantum Machine Learning (QML), which integrates quantum computing with machine learning techniques, presents powerful new tools for data processing and pattern recognition. This paper proposes a hybrid recommendation system that combines Quantum Hopfield Associative Memory (QHAM) with deep neural networks to improve the extraction and classification on the MovieLens 1M dataset. User archetypes are clustered into multiple unique groups using the K-Means algorithm and converted into polar patterns through the encoder's activation function. These polar patterns are then integrated into the variational QHAM-based hybrid recommendation model. The system was trained using the MSE loss over 35 epochs in an ideal environment, achieving an ROC value of 0.9795, an accuracy of 0.8841, and an F-1 Score of 0.8786. Trained with the same number of epochs in a noisy environment using a custom Qiskit AER noise model incorporating bit-flip and readout errors with the same probabilities as in real quantum hardware, it achieves an ROC of 0.9177, an accuracy of 0.8013, and an F-1 Score equal to 0.7866, demonstrating consistent performance. Additionally, we were able to optimize the qubit overhead present in previous QHAM architectures by efficiently updating only one random targeted qubit. This research presents a novel framework that combines variational quantum computing with deep learning, capable of dealing with real-world datasets with comparable performance compared to purely classical counterparts. Additionally, the model can perform similarly well in noisy configurations, showcasing a steady performance and proposing a promising direction for future usage in recommendation systems.

cs.IR↗

arXiv2025

Quantum Machine Learning for Energy-Efficient 5G-Enabled IoMT Healthcare Systems: Enhancing Data Security and Processing

Energy-efficient healthcare systems are becoming increasingly critical for Industry 5.0 as the Internet of Medical Things (IoMT) expands, particularly with the integration of 5G technology. 5G-enabled IoMT systems allow real-time data collection, high-speed communication, and enhanced connectivity between medical devices and healthcare providers. However, these systems face energy consumption and data security challenges, especially with the growing number of connected devices operating in Industry 5.0 environments with limited power resources. Quantum computing integrated with machine learning (ML) algorithms, forming quantum machine learning (QML), offers exponential improvements in computational speed and efficiency through principles such as superposition and entanglement. In this paper, we propose and evaluate three QML algorithms, which are UU†, variational UU†, and UU†-quantum neural networks (QNN) for classifying data from four different datasets: 5G-South Asia, Lumos5G 1.0, WUSTL EHMS 2020, and PS-IoT. Our comparative analysis, using various evaluation metrics, reveals that the UU†-QNN method not only outperforms the other algorithms in the 5G-South Asia and WUSTL EHMS 2020 datasets, achieving 100% accuracy, but also aligns with the human-centric goals of Industry 5.0 by allowing more efficient and secure healthcare data processing. Furthermore, the robustness of the proposed quantum algorithms is verified against several noisy channels by analyzing accuracy variations in response to each noise model parameter, which contributes to the resilience aspect of Industry 5.0. These results offer promising quantum solutions for 5G-enabled IoMT healthcare systems by optimizing data classification and reducing power consumption while maintaining high levels of security even in noisy environments.

quant-ph↗

arXiv2025

Quantum Repeater Chains via Cavity Magnon for Scalable Quantum Networks

Scalable quantum networks require quantum repeaters to overcome major challenges such as photon loss and decoherence in long-distance quantum communication. In this paper, we present a cavity-magnon quantum repeater architecture that exploits the frequency tunability and coherence characteristics of magnonic platforms to enable efficient entanglement swapping across multi-hop networks. Through comprehensive numerical simulations with realistic experimental parameters, we analyze system performance across diverse deployment scenarios and network scales, examining both short-range and long-distance implementations. We identify critical factors influencing performance and scalability, demonstrating that cavity-magnon systems represent a viable and promising quantum repeater platform with significant integration advantages over existing quantum memory technologies.

quant-ph↗

arXiv2025

Feature Prediction in Quantum Graph Recurrent Neural Networks with Applications in Information Hiding

Graphs are a fundamental representation of complex, nonlinear structured data across various domains, including social networks and quantum systems. Quantum Graph Recurrent Neural Networks (QGRNNs) have been proposed to model quantum dynamics in graph-based quantum systems, but their applicability to classical data remains an open problem. In this paper, we leverage QGRNNs to process classical graph-structured data. In particular, we demonstrate how QGRNN can reconstruct node features in classical datasets. Our results show that QGRNN achieves high feature reconstruction accuracy, leading to near-perfect classification. Furthermore, we propose an information hiding technique based on our QGRNN, where messages are embedded into a graph, then retrieved under certain conditions. We assess retrieval accuracy for different dictionary sizes and message lengths, showing that QGRNN maintains high retrieval accuracy, with minor degradation as complexity increases. These findings demonstrate the scalability and robustness of QGRNNs for both classical data processing and secure information hiding, paving the way for quantum-enhanced feature extraction, privacy-preserving computations, and quantum steganography.

quant-ph↗

arXiv2025

A Unified Framework for UAV-Based Free-Space Quantum Links: Beam Shaping and Adaptive Field-of-View Control

This paper develops a comprehensive analytical framework for modeling and performance evaluation of unmanned aerial vehicles (UAVs)-to-ground quantum communication links, incorporating key physical impairments such as beam divergence, pointing errors at both transmitter and receiver, atmospheric attenuation, turbulence-induced fading, narrow field-of-view (FoV) filtering, and background photon noise. To overcome the limitations of conventional wide-beam assumptions, we introduce a grid-based approximation for photon capture probability that remains accurate under tightly focused beams. Analytical expressions are derived for the quantum key generation rate and quantum bit error rate (QBER), enabling fast and reliable system-level evaluation. Our results reveal that secure quantum key distribution (QKD) over UAV-based free-space optical (FSO) links requires beam waists below 10 cm and sub-milliradian tracking precision to achieve Mbps-level key rates and QBER below $10^{-3}$. Additionally, we highlight the critical role of receiver FoV in balancing background noise rejection and misalignment tolerance, and propose adaptive FoV tuning strategies under varying illumination and alignment conditions. The proposed framework provides a tractable and accurate tool for the design, optimization, and deployment of next-generation airborne quantum communication systems.

eess.SP↗

arXiv2025

Physical Limits of Entanglement-Based Quantum Key Distribution over Long-Distance Satellite Links

Entanglement-based quantum key distribution (QKD) protocols, such as E91 and BBM92, offer strong information-theoretic security and are naturally suited for satellite-to-satellite QKD (SatQKD) links. However, implementing these protocols over long-distance inter-satellite free-space optical (FSO) channels poses critical physical-layer challenges that are not addressed in the existing literature. In particular, photon losses due to beam divergence, pointing errors, and background noise can severely degrade the key generation rate and quantum bit error rate (QBER), especially under narrow receiver field-of-view (FoV) constraints. This paper presents a comprehensive performance analysis of entanglement-based inter-satellite QKD, focusing on photon-level modeling and the impact of practical impairments. We develop analytical expressions for signal detection probabilities, background photon influence, multi-pair emissions, and QBER, incorporating key parameters such as link distance, transmitter tracking jitter, receiver misalignment, and photon pair generation rate. Simulation results reveal the nonlinear sensitivity of system performance to tracking error and FoV limitations, and highlight optimal parameter regimes that jointly maximize secret key rate while maintaining QBER below acceptable thresholds. The proposed model provides actionable design insights for reliable and efficient deployment of entanglement-based SatQKD systems.

eess.SP↗

arXiv2025

Single-photon emission modeling with statistical estimators for the exponential distribution

Single-photon sources are used in numerous quantum technologies, from sensing and imaging to communication, making the accurate modeling of their emissions essential. In this work, we propose a statistical framework for describing single-photon emission processes and implement estimators for the exponential distribution to quantify this phenomenon. Our approach provides a reliable method for estimating the radiative decay time, represented by the inverse rate parameter, which is crucial in quantum optics applications. We explore several statistical estimators, including maximum likelihood estimation, minimum-variance unbiased estimator, and best linear unbiased estimator. To validate our theoretical methods, we test the proposed estimators on experimental data, demonstrating their applicability in real-world settings. We also evaluate the performance of these estimators when dealing with censored data, a frequent limitation in photon emission experiments. The analysis allows us to track the performance of the proposed estimators as the amount of available data decreases, providing insights into their reliability for modeling single-photon emission events under limited resources.

quant-ph↗

arXiv2025

QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems

A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human-computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a Quantum Support Vector Machine (QSVM) with a Quantum Neural Network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.

quant-ph↗

arXiv2025

Device-Independent Quantum Key Distribution: Protocols, Quantum Games, and Security

Quantum Key Distribution (QKD) is based on the laws of quantum mechanics to enable provably secure communication. Despite its theoretical security promise, practical QKD systems are vulnerable to serious attacks, including side-channel attacks and detector loopholes, and assumes a trusted device characterization. Device-Independent Quantum Key Distribution (DIQKD) overcomes these limitations by relying solely on observed nonlocal correlations, certified through Bell inequality violations, thereby removing assumptions about the internal workings of the measurement devices. In this paper, we first review the foundational principles underlying DIQKD, including Bell tests and security definitions. We then examine a range of protocol designs, including CHSH-based schemes, and non-local game frameworks, alongside with their security proofs. We also assess recent experimental implementations and discuss source architectures, detection technologies, and finite-key analyses. Finally, we identify current open problems, such as noise tolerance, generation rates, and integration with quantum networks, and outline promising directions for future research to realize robust, high-performance DIQKD.

quant-ph↗

arXiv2025

Integrating Communication, Sensing, and Security: Progress and Prospects of PLS in ISAC Systems

The sixth generation of wireless networks defined several key performance indicators (KPIs) for assessing its networks, mainly in terms of reliability, coverage, and sensing. In this regard, remarkable attention has been paid recently to the integrated sensing and communication (ISAC) paradigm as an enabler for efficiently and jointly performing communication and sensing using the same spectrum and hardware resources. On the other hand, ensuring communication and data security has been an imperative requirement for wireless networks throughout their evolution. The physical-layer security (PLS) concept paved the way to catering to the security needs in wireless networks in a sustainable way while guaranteeing theoretically secure transmissions, independently of the computational capacity of adversaries. Therefore, it is of paramount importance to consider a balanced trade-off between communication reliability, sensing, and security in future networks, such as the 5G and beyond, and the 6G. In this paper, we provide a comprehensive and system-wise review of designed secure ISAC systems from a PLS point of view. In particular, the impact of various physical-layer techniques, schemes, and wireless technologies to ensure the sensing-security trade-off is studied from the surveyed work. Furthermore, the amalgamation of PLS and ISAC is analyzed in a broader impact by considering attacks targeting data confidentiality, communication covertness, and sensing spoofing. The paper also serves as a tutorial by presenting several theoretical foundations on ISAC and PLS, which represent a practical guide for readers to develop novel secure ISAC network designs.

cs.ET↗

arXiv2025

On the Vulnerability of Underwater Magnetic Induction Communication

Typical magnetic induction (MI) communication is commonly considered a secure underwater wireless communication (UWC) technology due to its non-audible and non-visible nature compared to acoustic and optical UWC technologies. However, vulnerabilities in communication systems inevitably exist and may lead to different types of attacks. In this paper, we investigate the eavesdropping attack in underwater MI communication to quantitatively measure the system's vulnerability under this attack. We consider different potential eavesdropping configuration setups based on the positions and orientations of the eavesdropper node to investigate how they impact the received voltage and secrecy at the legitimate receiver node. To this end, we develop finite-element-method-based simulation models for each configuration in an underwater environment and evaluate the received voltage and the secrecy capacity against different system parameters such as magnetic flux, magnetic flux density, distance, and orientation sensitivity. Furthermore, we construct an experimental setup within a laboratory environment to replicate the simulation experiments. Both simulation and lab experimental confirm the susceptibility of underwater MI communication to eavesdropping attacks. However, this vulnerability is highly dependent on the position and orientation of the coil between the eavesdropper and the legitimate transmitter. On the positive side, we also observe a unique behavior in the received coil reception that might be used to detect malicious node activities in the vicinity, which might lead to a potential security mechanism against eavesdropping attacks.

cs.CR↗

arXiv2025

Controlled quantum secure remote sensing

Quantum resources enable secure quantum sensing (SQS) of remote systems, offering significant advantages in precision and security. However, decoherence in the quantum communication channel and during the evolution of quantum states can erode these advantages. In this work, we first propose a general $N-$particle scheme that achieves Heisenberg-limited (HL) scaling for single-parameter estimation in the presence of an ideal quantum communication channel and encoding scenario. For non-ideal dynamics, we introduce a modified protocol incorporating local quantum optimal control (QOC) operations to address noise in SQS under generalized Pauli dephasing and parallel dephasing noise. We analyze two distinct scenarios: a noiseless communication channel with noisy evolution, and a noisy communication channel with noisy evolution. For the noisy channel, we model the link between the communicating parties as a depolarizing channel. The protocol leverages QOC operations to actively mitigate noise, enhancing the achievable quantum Fisher information (QFI) and the classical Fisher information (CFI) based on the chosen measurement strategy.

quant-ph↗

arXiv2025

QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction

Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card fraud detection and loan eligibility prediction has become increasingly challenging. Classical machine learning (ML) models have been used to solve these challenges; however, these approaches often encounter scalability, overfitting, and high computational costs due to complexity and high-dimensional financial data. Quantum computing (QC) and quantum machine learning (QML) provide a promising solution to efficiently processing high-dimensional datasets and enabling real-time identification of subtle fraud patterns. However, existing quantum algorithms lack robustness in noisy environments and fail to optimize performance with reduced feature sets. To address these limitations, we propose a quantum feature deep neural network (QFDNN), a novel, resource efficient, and noise-resilient quantum model that optimizes feature representation while requiring fewer qubits and simpler variational circuits. The model is evaluated using credit card fraud detection and loan eligibility prediction datasets, achieving competitive accuracies of 82.2% and 74.4%, respectively, with reduced computational overhead. Furthermore, we test QFDNN against six noise models, demonstrating its robustness across various error conditions. Our findings highlight QFDNN potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions while supporting sustainability through its resource-efficient design and minimal computational overhead.

quant-ph↗