arXiv subjects

Saif Al-Kuwari

Publications and source records attributed to Saif Al-Kuwari.

At least 91 records · Page 5Linked to original sources

arXiv2025

Holographic Consequences of Heterotic String Theory beyond its Supergravity Approximation

In this work, we study the effects of stringy corrections on the low energy effective action derived from heterotic string theory beyond its supergravity approximation. Compactifying the ten dimensional theory with these stringy corrections produces an effective action for a scalar field whose higher derivative term is governed by a single coefficient that depends on the internal volume, average curvature, and flux of the compactification manifold. The higher derivative coupling imported from compactification shifts the Breitenlohner Freedman stability bound by an amount set by the relative strengths of internal flux and curvature, relaxing it in flux dominated vacua and tightening it in curvature dominated ones. Furthermore, we analyze how the stringy corrections shift the scaling dimensions of the dual operators, track the resulting renormalization group flow, and investigate the higher derivative term using a holographic Lee Wick regulator. In holographic superconductors, stringy corrections lower the effective bulk mass and raise the critical temperature when flux dominates, but have the opposite effect when curvature dominates.

hep-th↗

arXiv2025

Quantum correlations and metrological advantage among Unruh-DeWitt detectors in de Sitter spacetime

A long-standing debate on Gibbons-Hawking (GH) decoherence centers on its unclear thermal nature. In this work, we investigate the robustness of quantum Fisher information (QFI) and local quantum uncertainty (LQU) in the presence of GH decoherence, using free-falling Unruh-DeWitt (UDW) detectors in de Sitter spacetime (dS-ST). The UDW detectors interact with a massless scalar field in dS-ST and are modeled as open quantum systems, with the field acting as the environment for which we use a master equation to describe their evolution. Our analysis investigates the roles of energy spacing, GH temperature, initial state preparation, and various de Sitter-invariant vacuum sectors on the optimization of QFI and LQU. We find that the optimal values of QFI and LQU depend on the selected de Sitter-invariant vacuum sector and increase with larger energy spacing. Our findings reveal that QFI exhibits resilience to GH decoherence, maintaining a pronounced local peak across a wider range of parameters. This robustness can be further enhanced through strategic initial state preparation and increased energy spacing, resulting in a higher maximum QFI value even under significant environmental decoherence. Our results underscore the critical role of GH thermality in governing QFI and LQU, offering valuable insights for advances in relativistic quantum metrology (RQM).

quant-ph↗

arXiv2025

Performance of a Superconducting Quantum Battery

Finding a quantum battery model that demonstrates a quantum advantage while remaining feasible for experimental production is a considerable challenge. Here, a superconducting quantum battery (SQB) model that exhibits such an advantage is introduced. The model consists of two coupled superconducting qubits that interact during the unitary charging process while remaining in equilibrium with a thermal reservoir. First, the model is described, evidence of the quantum advantage is provided, and then the fabrication process of the battery is discussed using superconducting qubits. Analytical expressions for the ergotropy, instantaneous power, and capacity of the SQB, as well as their connection to quantum coherence are derived. It is demonstrated that leveraging the collective effects of Josephson energies and the coupling energy between qubits allows for optimization, resulting in improved energy redistribution and a significant enhancement in charging efficiency. This work highlights the complexities of tuning system parameters, which increase the potential for work extraction from the SQB, providing a deeper understanding of the charging mechanisms involved. These findings can be applied to superconducting quantum circuit battery architectures, underscoring the feasibility of efficient energy storage in these systems. These results pave the way for proposals of new superconducting devices, emphasizing their potential for efficient energy storage.

quant-ph↗

arXiv2025

Jahn-Teller Effect for Controlling Quantum Correlations in Hexanuclear Fe$^{3+}$ Magnets

We investigate the low-temperature magnetic and quantum properties of hexanuclear Fe$^ {3+}_6$ complexes under an external magnetic field. We primarily study the impact of competing exchange interactions and their asymmetries induced by the Jahn-Teller distortion on the quantum properties of the complexes. The inequality in exchange interactions lifts the ground-state degeneracy that gives rise to complex quantum behavior. By constructing the ground-state phase diagram and analyzing magnetization, we identify key magnetic phases and critical phenomena. We further quantify quantum correlations using tripartite entanglement negativity and conditional von Neumann entropy to unveil how the Jahn-Teller effect enhances intra-triangle entanglement while modulating inter-triangle correlations. Our findings highlight the Fe$^{3+}_6$ complex as a promising molecular platform for tunable quantum correlations, with potential applications in quantum information processing and molecular qubits.

cond-mat.mtrl-sci↗

arXiv2025

Optimizing Low-Energy Carbon IIoT Systems with Quantum Algorithms: Performance Evaluation and Noise Robustness

Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance. Although classical algorithms manage energy efficiency and data processing within these systems, they often face scalability and real-time processing limitations. Quantum algorithms offer a solution to these challenges by delivering faster computations and improved optimization, thereby enhancing both the performance and sustainability of low-energy carbon IoT systems. Therefore, we introduced three quantum algorithms: quantum neural networks utilizing Pennylane (QNN-P), Qiskit (QNN-Q), and hybrid quantum neural networks (QNN-H). These algorithms are applied to two low-energy carbon IoT datasets room occupancy detection (RODD) and GPS tracker (GPSD). For the RODD dataset, QNN-P achieved the highest accuracy at 0.95, followed by QNN-H at 0.91 and QNN-Q at 0.80. Similarly, for the GPSD dataset, QNN-P attained an accuracy of 0.94, QNN-H 0.87, and QNN-Q 0.74. Furthermore, the robustness of these models is verified against six noise models. The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future low-energy carbon IoT systems. These advancements pave the way for more sustainable and efficient IoT infrastructures, significantly minimizing energy consumption while maintaining optimal device performance.

quant-ph↗

arXiv2025

QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU† method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate the key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.

quant-ph↗

arXiv2025

Benchmarking Online Object Trackers for Underwater Robot Position Locking Applications

Autonomously controlling the position of Remotely Operated underwater Vehicles (ROVs) is of crucial importance for a wide range of underwater engineering applications, such as in the inspection and maintenance of underwater industrial structures. Consequently, studying vision-based underwater robot navigation and control has recently gained increasing attention to counter the numerous challenges faced in underwater conditions, such as lighting variability, turbidity, camera image distortions (due to bubbles), and ROV positional disturbances (due to underwater currents). In this paper, we propose (to the best of our knowledge) a first rigorous unified benchmarking of more than seven Machine Learning (ML)-based one-shot object tracking algorithms for vision-based position locking of ROV platforms. We propose a position-locking system that processes images of an object of interest in front of which the ROV must be kept stable. Then, our proposed system uses the output result of different object tracking algorithms to automatically correct the position of the ROV against external disturbances. We conducted numerous real-world experiments using a BlueROV2 platform within an indoor pool and provided clear demonstrations of the strengths and weaknesses of each tracking approach. Finally, to help alleviate the scarcity of underwater ROV data, we release our acquired data base as open-source with the hope of benefiting future research.

cs.RO↗

arXiv2025

QNN-VRCS: A Quantum Neural Network for Vehicle Road Cooperation Systems

The escalating complexity of urban transportation systems, exacerbated by factors such as traffic congestion, diverse transportation modalities, and shifting commuter preferences, necessitates the development of more sophisticated analytical frameworks. Traditional computational approaches often struggle with the voluminous datasets generated by real-time sensor networks, and they generally lack the precision needed for accurate traffic prediction and efficient system optimization. This research integrates quantum computing techniques to enhance Vehicle Road Cooperation Systems (VRCS). By leveraging quantum algorithms, specifically $UU^{\dagger}$ and variational $UU^{\dagger}$, in conjunction with quantum image encoding methods such as Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR), we propose an optimized Quantum Neural Network (QNN). This QNN features adjustments in its entangled layer structure and training duration to better handle the complexities of traffic data processing. Empirical evaluations on two traffic datasets show that our model achieves superior classification accuracies of 97.42% and 84.08% and demonstrates remarkable robustness in various noise conditions. This study underscores the potential of quantum-enhanced 6G solutions in streamlining complex transportation systems, highlighting the pivotal role of quantum technologies in advancing intelligent transportation solutions.

quant-ph↗

arXiv2025

SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems

Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in sentiment analysis. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. Additionally, they are frequently insensitive to input variations. In this paper, we propose a novel hybrid approach for sentiment analysis called the Quantum Fuzzy Neural Network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical sentiment analysis algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results demonstrate that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with sentiment analysis on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyses different forms of textual data, thereby enhancing sentiment classification and insights associated with sentiment analysis.

cs.CL↗

arXiv2024

Kitaev Quantum Batteries: Super-Extensive Scaling of Ergotropy in 1D Spin$-1/2$ $XY-Γ(γ)$ Chain

We investigate the performance of a novel model based on a one-dimensional (1D) spin-$1/2$ Heisenberg $XY-Γ(γ)$ quantum chain, also known as 1D Kitaev chain, as a working medium for a quantum battery (QB) in both closed and open system scenarios. We analyze the closed QB scenario by analytically evaluating ergotropy across different spin-spin couplings, anisotropies in spin interactions, Zeeman field strengths, charging field intensities, $Γ$ interactions, and temperature. Our results indicate that the ergotropy is highly dependent on spin-spin coupling and anisotropy. Under variable parameters, an increase in the spin-spin coupling strength displays quenches and exhibits non-equilibrium trends in ergotropy. After a quench, ergotropy may experience a sharp increase or drop, suggesting optimal operational conditions for QB performance. In the open QB scenario, we examine spin chains of sizes $2 \leq N \leq 8$ under the influence of dephasing, focusing on the evolution of ergotropy. We study two charging schemes: parallel charging, where spins are non-interacting, and collective charging, involving spin-spin coupling. In the former, increased Zeeman field strength enhances both the peak ergotropy and charging rate, although without any quantum advantage or super-extensive scaling. In the latter, increasing spin-spin coupling might not achieve super-extensive scaling without introducing anisotropy in the spin-spin interaction. Our results suggest that optimal QB performance and a quantum advantage in scaling can be achieved by leveraging anisotropic spin-spin couplings and non-zero $Γ$ interactions, allowing for faster charging and higher ergotropy under super-extensive scaling conditions up to $α=1.24$ for the given size of the spin chain.

quant-ph↗

arXiv2024

Tripartite measurement uncertainty in Schwarzschild space-time

The effect of Hawking radiation on tripartite measurement uncertainty in a Schwarzschild black hole background is analyzed in this study. Two scenarios are examined. In the first, quantum memory particles approach a Schwarzschild black hole and are positioned near the event horizon, while the particle being measured remains in the asymptotically flat region. In the second scenario, the measured particle moves toward the black hole, and the quantum memories stay in the asymptotically flat region. This study considers two initial quantum states, namely GHZ and W states. Our findings reveal that in both cases, measurement uncertainty increases steadily with rising Hawking temperature. When comparing the GHZ and W states, the GHZ state initially exhibits lower measurement uncertainty at low Hawking temperatures than the W state, indicating greater resilience to Hawking radiation. Additionally, when the quantum memories remain in the asymptotically flat region while the measured particle falls toward the black hole, the uncertainties for GHZ and W states do not align at high temperatures. The GHZ state consistently demonstrates lower measurement uncertainty, showcasing its superior robustness against Hawking radiation.

gr-qc↗

arXiv2024

Entanglement-enhanced optimal quantum metrology

Quantum optimal control (QOC) schemes can be employed to enhance the sensitivity of quantum metrology (QM) protocols undergoing Markovian noise, which can limit their precision to a standard quantum limit (SQL)-like scaling. In this paper, we propose a QOC scheme for QM that leverages entanglement and optimized coupling interactions with an ancillary system to provide enhanced metrological performance under general Markovian dynamics. We perform a comparative analysis of our entanglement-enhanced scheme against the unentangled scheme conventionally employed in QOC-enabled QM for varying evolution times and decoherence levels, revealing that the entanglement-enhanced scheme enables significantly better noise performance, even when a noisy ancilla is employed. We further extend our investigation to time-inhomogeneous noise models, specifically focusing on a noisy frequency estimation scenario within a spin-boson bath, and evaluate the protocol's performance under completely dissipative and dephasing dynamics. Our findings indicate that, in certain situations, schemes employing coherent control of a single particle are severely limited. In such cases, employing the entanglement-enhanced scheme can provide improved performance.

quant-ph↗

arXiv2024

Ergotropy and capacity optimization in Heisenberg spin-chain quantum batteries

This study examines the performance of finite spin quantum batteries (QBs) using Heisenberg spin models with Dzyaloshinsky-Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions. The QBs are modeled as interacting quantum spins in local inhomogeneous magnetic fields, inducing variable Zeeman splitting. We derive analytical expressions for the maximal extractable work, ergotropy and the capacity of QBs, as recently examined by Yang et al. [Phys. Rev. Lett. 131, 030402 (2023)]. These quantities are analytically linked through certain quantum correlations, as posited in the aforementioned study. Different Heisenberg spin chain models exhibit distinct behaviors under varying conditions, emphasizing the importance of model selection for optimizing QB performance. In antiferromagnetic (AFM) systems, maximum ergotropy occurs with a Zeeman splitting field applied to either spin, while ferromagnetic (FM) systems benefit from a uniform Zeeman field. Temperature significantly impacts QB performance, with ergotropy in the AFM case being generally more robust against temperature increases compared to the FM case. Incorporating DM and KSEA couplings can significantly enhance the capacity and ergotropy extraction of QBs. However, there exists a threshold beyond which additional increases in these interactions cause a sharp decline in capacity and ergotropy. This behavior is influenced by temperature and quantum coherence, which signal the occurrence of a sudden phase transition. The resource theory of quantum coherence proposed by Baumgratz et al. [Phys. Rev. Lett. 113, 140401 (2014)] plays a crucial role in enhancing ergotropy and capacity. However, ergotropy is limited by both the system's capacity and the amount of coherence. These findings support the theoretical framework of spin-based QBs and may benefit future research on quantum energy storage devices.

quant-ph↗

arXiv2024

Non-Hermitian Discrete Time Crystals

Discrete time crystals (DTC) exhibit a special non-equilibrium phase of matter in periodically driven many-body systems with spontaneous breaking of time translational symmetry. The presence of decoherence generally enhances thermalization and destroys the coherence required for the existence of DTC. In this letter, we devise a mechanism for establishing a stable DTC with period-doubling oscillations in an open quantum system that is governed by a properly tailored non-Hermitian Hamiltonian. We find a specific class of non-reciprocal couplings in our non-Hermitian dynamics which prevents thermalization through eigenstate ordering. Such choice of non-Hermitian dynamics, significantly enhances the stability of the DTC against imperfect pulses. Through a comprehensive analysis, we determine the phase diagram of the system in terms of pulse imperfection.

quant-ph↗

arXiv2024

A Study on Thermal Quantum Resources and Probabilistic Teleportation in Spin-1/2 Heisenberg XYZ+DM+KSEA Model under Variable Zeeman Splitting

We investigate the behavior of various measures of quantum coherence and quantum correlation in the spin-1/2 Heisenberg XYZ model with added Dzyaloshinsky-Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions at a thermal regime described by a Gibbs density operator. We aim to understand the restricted hierarchical classification of different quantum resources, where Bell nonlocality $\subseteq$ quantum steering $\subseteq$ quantum entanglement $\subseteq$ quantum discord $\subseteq$ quantum coherence. This hierarchy highlights the increasingly stringent conditions required as we move from quantum coherence to more specific quantum phenomena. In order to enhance quantum coherence, quantum correlation, and fidelity of teleportation, our analysis encompasses the effects of independently provided sinusoidal magnetic field control as well as DM and KSEA interactions on the considered system. The results reveal that enhancing the entanglement or quantum correlation of the channel does not always guarantee successful teleportation or even an improvement in teleportation fidelity. Thus, the relationship between teleportation fidelity and the channel's underlying quantum properties is intricate. Our study provides valuable insights into the complex interplay of quantum coherence and correlation hierarchy, offering potential applications for quantum communication and information processing technologies.

quant-ph↗

arXiv2024

Towards Scalable Quantum Key Distribution: A Machine Learning-Based Cascade Protocol Approach

Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99\% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol's integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication.

quant-ph↗

arXiv2024

Quantum Characteristics Near Event Horizons

We investigate quantum characteristics around Schwarzschild black hole, exploring various quantum resources and their interplay in curved space-time. Our analysis reveals intriguing behaviors of quantum coherence, global and genuine multipartite entanglement, first-order coherence, and mutual information in different scenarios. Initially, we consider three particles shared among Alice, Bob, and Charlie in a Minkowski space far from the event horizon, where these particles are correlated via GHZ-type correlation. While Alice's particle remains in Minkowski space, Bob and Charlie accelerate towards the event horizon, experiencing black hole evaporation and generating antiparticles correlated via the Hawking effect. We employ the Kruskal basis formulation to derive a penta-partite pure state shared among particles inside and outside the event horizon. Investigating different scenarios among particles both inside and outside the event horizon, we observe how quantum resources evolve and distribute among consideration of different particles with Hawking temperature and mode frequency. The trade-off relationship between first-order coherence and concurrence fill persists, indicating the intricate interplay between coherence and entanglement. Notably, the mutual information between external observers and particles inside the black hole becomes non-zero, deepening our understanding of quantum effects in curved space-time and shedding light on the quantum nature of the black hole. We believe that these findings will pave the way for future investigations into the fundamental quantum mechanical aspects of gravity under extreme environments.

quant-ph↗

arXiv2024

Physically Accessible and Inaccessible Quantum Correlations of Dirac Fields in Schwarzschild Spacetime

In this study, we investigate the influence of Hawking decoherence on the quantum correlations of Dirac fields between \textit{Alice} and \textit{Bob}. Initially, they share a \textit{Gisin} state near the Schwarzschild black hole (SBH) in an asymptotically flat region. Then, \textit{Alice} remains stationary in this region, while \textit{Bob} hovers near the event horizon (EH) of the SBH. We expect that \textit{Bob}, using his excited detector, will detect a thermal Fermi-Dirac particle distribution. We assess the quantum correlations in the evolved \textit{Gisin} state using quantum consonance and uncertainty-induced non-locality across physically accessible, physically inaccessible, and spacetime regions. Our investigation examines how these measures vary with Hawking temperature, Dirac particle frequency, and the parameters of the initial \textit{Gisin} state. Additionally, we analyze the distribution of these quantum correlation measures across all possible regions, noting a redistribution towards the physically inaccessible region. Our findings demonstrate that Hawking decoherence reduces the quantum correlations of Dirac fields in the physically accessible region, with the extent of reduction depending on the initial state parameters. Moreover, as Hawking decoherence intensifies in the physically inaccessible and spacetime regions, the quantum correlations of Dirac fields reemerge and ultimately converge to specific values at infinite Hawking temperature. These results contribute to our understanding of quantum correlation dynamics within the framework of relativistic quantum information (RQI).

quant-ph↗