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Saif Al-Kuwari

Publications and source records attributed to Saif Al-Kuwari.

At least 37 records · Page 2Linked to original sources
arXiv2026

Counterdiabatic Raman Atom Optics for Compact High-Sensitivity Gravimetry

Large-momentum-transfer (LMT) atom interferometry provides a route toward enhanced inertial sensitivity in compact quantum sensors, but its scalability is limited by the accumulation of pulse-transfer errors across long Raman pulse sequences. We investigate theoretically the use of stimulated Raman shortcut-to-adiabatic passage (STIRSAP) for high-fidelity LMT atom optics in a Mach--Zehnder interferometer geometry. The counterdiabatic correction is encoded directly into the Raman pulse envelopes, eliminating the need for auxiliary microwave or radio-frequency control fields. Numerical simulations based on an effective Raman model show that $1~μ\mathrm{s}$ STIRSAP pulses achieve single-pulse transfer fidelities of $F_π= 0.99902$ while maintaining negligible pulse-time overhead even at high momentum order. We analyze the resulting tradeoff between interferometric phase enhancement and compound contrast decay and identify an unconstrained shot-noise optimum near $n\approx270$. The analysis further shows that practical operation at extreme LMT order is constrained by wave-packet separation, vibration noise, Doppler detuning, and accumulated systematic effects rather than by pulse duration itself. These results establish superadiabatic Raman control as a promising approach for scalable high-fidelity atom optics and clarify the physical limitations governing compact high-order atom interferometers.

quant-ph↗
arXiv2026

QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition

Traffic sign recognition is crucial for intelligent transportation and autonomous driving, as it can improve driving efficiency and ensure road safety. However, traditional recognition methods are based on large datasets and intensive computation, which limits their real-time applicability. Spiking Neural Networks (SNNs) offer a biologically inspired, energy-efficient alternative due to their spatiotemporal processing capabilities, but suffer from information loss and vanishing gradients during training. To overcome these limitations, this study proposes a Quantum Deep-supervised Spiking Neural Network (QDS-SNN) that integrates Quantum Neural Networks (QNNs) for efficient, low-power deep supervision. Using quantum superposition and entanglement, QNNs enable expressive representations and parallel computation, thereby enhancing performance without compromising energy efficiency. The proposed QDS-SNN incorporates a temporally and spatially adaptive LIF (TSA-LIF) neuron and a quantum-assisted classifier module (QACM) to mitigate gradient issues and improve training effectiveness. This study conducts experiments on the PennyLane quantum simulation platform, and the results show that QDS-SNN achieves 99.72\% accuracy on the GTSRB dataset in only 6 time steps -- outperforming the MS-ResNet baseline by 1.32\% while reducing energy consumption by 55.77\%. In the TSRD dataset, it achieves 97.90\% accuracy while reducing energy use to 52.68\% of the baseline. These results demonstrate that QDS-SNN offers a high-performance, energy-efficient solution for traffic sign recognition in intelligent transportation systems.

cs.NE↗
arXiv2026

AML-QKD: Adaptive Machine Learning Framework for Real-time Parameter Tuning in QKD

Despite the robust security guarantees of Quantum Key Distribution (QKD), practical deployment is hindered by dynamic channel noise and complex parameter optimization. We propose AML-QKD, a protocol-agnostic machine learning framework designed to maximize the Secure Key Rate (SKR) and minimize the Quantum Bit Error Rate (QBER) across the BB84, E91, and COW protocols. AML-QKD integrates Temporal Convolutional Networks (TCNs) for short-horizon forecasting of channel fluctuations, using a Proximal Policy Optimization (PPO) agent for real-time parameter tuning, while strictly adhering to composable security constraints. Simulations under realistic depolarizing and amplitude-damping noise demonstrate a 14-25% increase in median SKR and a reduction in median QBER from 3.0% to 1.5%. Furthermore, an exploratory Quantum Reinforcement Learning (QRL) extension reveals a distinct quantum advantage for entanglement-based protocols (E91), achieving a 29.2% throughput gain by natively processing non-local correlations. Our findings suggest that AML-QKD can offer a potentially resilient, security-preserving control architecture for next-generation quantum networks.

quant-ph↗
arXiv2026

Quantum Speed Limit under Calibration Uncertainty

Standard quantum speed limits presuppose exactly known parameters, overestimating operational speed under calibration uncertainty. We introduce a projected speed limit based on the quantum Fisher information that profiles out these nuisance parameters on a quotient manifold. We derive constructive bounds for general Markovian evolution using sensitivity equations. Applying this to Jaynes--Cummings sensors, we obtain explicit detuning tolerances and quantify speed limits arising from field-dependent Purcell loss. This framework turns geometric bounds into concrete design rules for calibration and interrogation time.

quant-ph↗
arXiv2026

Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation

With the advent of sixth-generation (6G) mobile communication technology, vehicle-to-everything (V2X) communication faces unprecedented challenges in communication efficiency, system generalization capabilities, and model collaboration. Conventional machine learning struggles with high-dimensional state spaces, slow convergence, and poor generalization under heterogeneous V2X nodes, rapidly varying channels, and multimodal sensing data in V2X systems. To address these issues, we propose a quantum-enhanced framework for V2X communication and model aggregation that targets efficient, robust, and intelligent transportation in 6G, which includes four modules: the channel-adaptive semantic communication module, the multimodal fusion module, the model transfer module, and the federated aggregation module. Specifically, the channel-adaptive semantic communication module leverages quantum convolutional neural networks (CNN) and quantum distortion metrics to enable efficient transmission and strong generalization across diverse conditions. The multimodal fusion module exploits quantum attention and entanglement to compress features and associate semantics across heterogeneous data. The model transfer module employs quantum reinforcement learning to model decision-making and improve adaptability in dynamic environments. The federated aggregation module integrates quantum tensor decomposition with backpropagation-based corrections to provide privacy preservation with low overhead and to strengthen global model robustness. This work outlines a new paradigm for communication and model collaboration in future 6G intelligent transportation.

quant-ph↗
arXiv2026

BARFI-Q: Quantum-Enhanced Block Attention Residual Fusion Framework for Multivariate Time-Series Forecasting in Atom Interferometry

Atom interferometry generates heterogeneous multivariate temporal streams governed by phase evolution, fringe dynamics, control variables, and auxiliary sensing measurements. Accurate forecasting of these signals is important for predictive monitoring, phase correction, and intelligent quantum sensing, but it requires effective modeling of long-range temporal dependencies and interactions among multiple sensing sources. This paper proposes BARFI-Q, a Quantum-Enhanced Block Attention Residual Fusion framework for multivariate time-series forecasting in atom interferometry. BARFI-Q integrates patch-based embedding, dual-branch temporal modeling, hierarchical fusion, adaptive block-attention residual aggregation, and a quantum feature-mapping module. Unlike conventional Transformer-based forecasting models with fixed additive residual paths, BARFI-Q adaptively reuses cross-depth information and enhances the fused latent representation through quantum feature mapping. To respect phase periodicity, the forecasting target is represented in circular space using sine and cosine components. Experiments show that BARFI-Q consistently outperforms strong baseline models across repeated runs and different historical window sizes. Fusion ablation results further confirm the benefit of jointly modeling channel-wise and spatial feature interactions. These results indicate that multiscale temporal learning, hierarchical fusion, adaptive residual routing, and quantum-enhanced latent transformation provide an effective framework for atom-interferometric time-series forecasting.

quant-ph↗
arXiv2026

Chip-scale superconducting quantum gravimeter combining a SQUID, a transmon, and a nanomechanical resonator

Precise gravitational measurements are vital for geophysics and inertial navigation, but compact gravimeters with high measurement bandwidth remain difficult to realize. We propose and analyze a chip-scale superconducting gravimeter in which a flux-tunable transmon qubit is coupled to a high quality factor ($Q_m$) nanomechanical beam. The beam is embedded in a SQUID loop placed in parallel with the qubit's flux-tunable SQUID; gravity induced beam displacement therefore modulates the qubit frequency through the SQUID flux and is mapped onto the qubit's geometric phase. A stroboscopic readout at mechanical revival times suppresses qubit mechanics dephasing, yielding a projected sensitivity of $10^2$--$10^3\,\mathrm{nGal}/\sqrt{\mathrm{Hz}}$ with sub-millisecond interrogation times. Electrical \emph{in situ} tunability and microwave-based calibration make this architecture a practical route toward compact, high-bandwidth on-chip gravimetry.

quant-ph↗
arXiv2026

Nonlinearity-enhanced Quantum Sensing in Discrete Time Crystal Probes

Discrete time crystals are non-equilibrium phases of matter in periodically driven systems, characterized by robust subharmonic oscillations and broken discrete time-translation symmetry. Their long-lived coherent dynamics and resilience to imperfections make them promising resources for quantum sensing. A disorder-free discrete-time crystal probe can provide the quantum-enhanced estimation of the coupling parameter. Here, we extend this sensing mechanism to nonlinear interactions and show that this nonlinear profile strongly enhances the sensing precision by increasing the system-size scaling exponent of the quantum Fisher information. Our analytical discussion separates a rigorous seminorm upper bound from the physically relevant scaling realized by product-state probes in the time crystal regime. Numerically, we find that the quantum Fisher information retains its quadratic long-time growth with the number of Floquet cycles, while its system-size exponent increases approximately linearly with the nonlinearity exponent, identifying nonlinearity as a resource for quantum-enhanced sensitivity. We further show that stronger nonlinearities shrink the time crystal stability window, making the probe more sensitive to small deviations from the resonant condition. We also analyze the effect of imperfect pulses and show that such imperfections can enhance, rather than suppress, the information encoded in the evolved state. Finally, we discuss a digital implementation of the nonlinear DTC sensing protocol using superconducting qubits.

quant-ph↗
arXiv2026

Quantum gravimetry with intrinsic quantum time uncertainty

We study quantum gravimetry when the interrogation time carries intrinsic uncertainty, motivated by a fundamental limit on temporal resolution associated with the energy--time uncertainty relation. For linearly gravity-coupled gravimeters, we obtain the effective gravity information by profiling the interrogation time from the two-parameter quantum Fisher information (QFI) matrix. In this class, the time-information block is quadratic in the gravitational parameter, and for quadratic background dynamics, the gravity--time cross term becomes affine in $g$. These properties yield a normalized expression for the fraction of standard single-parameter gravity QFI that remains once interrogation time is treated as a nuisance parameter, with an affine numerator and a Lorentzian denominator. We work out these results in three benchmark models: a freely falling Gaussian wavepacket, the Kasevich--Chu light-pulse atom interferometer, and an idealized closed-unitary optomechanical model. The Gaussian free-fall benchmark yields an exact closed-form expression for the effective gravity information and shows explicitly how nuisance-time profiling suppresses the momentum-spread-dependent part of the standard single-parameter gravity QFI. In the Kasevich--Chu interferometer, internal state population readout gives a rank-deficient measured two-parameter geometry unless independent timing information is supplied, whereas full access to the final motional and internal states restores a full-rank geometry with retention controlled by the competition between initial velocity spread and gravitationally accumulated motion. In atom-interferometric benchmarks, the framework yields explicit conditions for minimizing nuisance-time information loss, together with corresponding constraints on momentum spread, spatial localization, and long-interrogation-time operation.

quant-ph↗
arXiv2026

Exponentially-enhanced Weak-field Sensing with Quantum Stark Localization

Stark-localized quantum probes have recently been shown to enable quantum-enhanced weak-field sensing with polynomial or super-polynomial scaling. In this paper, we show that the spatial geography of the encoded field can elevate this advantage to a genuine exponential scaling. We study a one-dimensional Stark probe subject to an exponential gradient profile, \(V_j=e^{aj}\), and analyze its metrological performance in both equilibrium and non-equilibrium regimes, for single-particle and interacting many-body settings. In the equilibrium single-particle case, we derive an analytical lower bound showing that the quantum Fisher information grows exponentially with system size, and confirm numerically that this enhancement persists throughout the extended phase and at the localization transition. We further show that the same exponential scaling survives for mid-spectrum eigenstates and in the interacting many-body regime. This advantage remains intact under a fair resource analysis because the relevant preparation gap closes only algebraically, so the polynomial preparation overhead cannot offset the exponential gain in sensitivity. In the non-equilibrium regime, a simple product-state initialization followed by free evolution already retains exponential enhancement, eliminating the need for cooling, adiabatic preparation, or operation within a narrowly tuned sensing window. Finally, we outline a superconducting implementation based on flux-tunable transmon qubits with graded mutual inductive coupling to a common sensing bus. Our results identify exponentially graded Stark potentials as a distinct and experimentally plausible route to weak-field sensing with exponentially improving precision.

quant-ph↗
arXiv2026

Topological Device-Independent Quantum Key Distribution Using Majorana-Based Qubits

Device-independent quantum key distribution (DI-QKD) provides the highest level of cryptographic security by certifying secrecy through observed Bell inequality violations, independent of the internal device physics. However, the transition from theory to practice is obstructed by the dual challenge of closing the detection loophole and achieving viable key rates over fiber distances. In this paper, we present a comprehensive theoretical framework for DI-QKD implemented on topological Majorana Zero Mode (MZM) processors. While MZMs offer a native parity-readout basis that simplifies Bell-state measurement, their viability as QKD nodes is fundamentally constrained by the interplay between storage latency and quasiparticle poisoning. We bridge the gap between microscopic hardware noise and macroscopic security by: (i) developing a hardware-native error model that maps MZM-specific processes, including poisoning rates, braid infidelities, and readout anisotropy, directly to the CHSH Bell parameter $S$; (ii) introducing a loss-disciplined protocol that monitors setting-conditional efficiencies to strictly enforce detection-loophole closure in a heralded architecture; and (iii) providing a composable finite-size security proof based on the Entropy Accumulation Theorem (EAT). Our analysis reveals that while topological protection stabilizes the system against calibration drift, the achievable secure distance is strictly bounded by the poisoning-induced visibility collapse during the photonic round-trip time. We identify specific hardware thresholds, particularly the suppression of poisoning rates to $Γ_p τ_{\text{max}} \ll 1$ and high-fidelity sensor integration, as the critical path for viable topological quantum networks.

quant-ph↗
arXiv2026

Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning

Generative Adversarial Networks (GANs) have demonstrated immense potential in synthesizing diverse and high-fidelity images. However, critical questions remain unanswered regarding how quantum principles might best enhance their representational and computational capacity. In this paper, we investigate hybrid quantum-classical GAN architectures supplemented by transfer learning to systematically examine whether incorporating Variational Quantum Circuits (VQCs) into the generator, the discriminator, or both improves performance over a fully classical baseline. Our findings indicate that fully hybrid models, which incorporate VQCs in both the generator and the discriminator, produce images with higher quality and achieve more favorable quantitative metrics compared to their fully classical counterparts. In particular, placing the quantum block in the generator appears to accelerate the early emergence of visual structure, whereas placing it in the discriminator slows early visual convergence but improves the final quantitative quality metric. Incorporating quantum blocks into both networks yields the strongest overall performance. Moreover, the model sustains comparable performance even when the dataset size is reduced. Overall, the results underscore that carefully integrating quantum computing with classical adversarial training and pretrained feature extraction can enrich GAN-based image synthesis. These insights open avenues for future work on higher-resolution tasks, alternative quantum circuit designs, and experimentation with emerging quantum hardware.

quant-ph↗
arXiv2026

Entanglement certification in bulk nonlinear crystals for degenerate and non-degenerate SPDC: spectral filter effects on transverse spatial correlations

Spatial correlations of photon pairs from spontaneous parametric down-conversion (SPDC) underpin quantum imaging and entanglement certification. We present the first systematic study of spectral filter bandwidth effects on transverse spatial correlations in bulk Type-I BBO for degenerate and non-degenerate configurations. In the far field, the degenerate conditional momentum width is pump-limited and filter-invariant, while non-degenerate configurations exhibit monotonic growth in both marginal and conditional momentum widths -- with the walk-off axis $\approx 100$ times more sensitive than the non-walk-off axis. In the near field, we identify a previously unreported flat-dip-rise profile: the conditional position width narrows by $\approx 10\%$ at an optimal bandwidth $Δ_\mathrm{dip} \approx 1.35\,Δλ_\mathrm{SPDC}$ before rising due to geometric displacement. When the filter is placed on the idler arm, the dip shifts by the exact factor $(λ_i/λ_s)^2$. Both results are universal for any non-degenerate SPDC source, requiring only a finite crystal length, $dθ/dλ\neq 0$, and incoherent spectral averaging. The Reid EPR uncertainty product is consistently smaller on the walk-off axis -- a structural advantage of bulk birefringent geometry absent in quasi-phase-matched sources. The optimal filter bandwidth $Δ_F = Δ_\mathrm{dip}$ is determined entirely by the intrinsic phase-matching bandwidth of the crystal and is directly readable from the X-entanglement spectral width of the source.

quant-ph↗
arXiv2026

Quantum Mpemba Effect in a Four-Site Bose-Hubbard Model

We investigate relaxation-order inversion, known as the quantum Mpemba effect (QME), in a minimal open many-body system called a one-dimensional four-site Bose--Hubbard chain governed by Lindblad dynamics with local number dephasing. Families of thermal initial states are prepared at a fixed temperature and evolved under a common reference Liouvillian toward the same stationary state. Relaxation is characterized using four complementary diagnostics: trace distance, quantum relative entropy, symmetry-projected entropy imbalance (entanglement asymmetry), and the $\ell_{1}$-norm of coherence in the Fock basis. We find that QME emerges robustly in -the clean interacting regime, where on-site interactions redistribute the overlaps of initial states with slow Liouvillian decay modes, enabling states initially farther from equilibrium to converge faster at late times. In contrast, the noninteracting limit exhibits a monotonic relaxation hierarchy across all metrics. Introducing a linear Stark potential or random on-site disorder suppresses relaxation and eliminates QME signatures by inhibiting transport-assisted mixing and enhancing the dominance of slow modes. Within the explored parameter regime, the Stark field induces significantly stronger retardation than disorder. We further show that symmetry-projected entropy imbalance is particularly sensitive to charge-sector decoherence in reduced subsystems and provides a stringent probe of QME in bosonic platforms. Our results elucidate the essential role of interactions in enabling anomalous relaxation in open lattice systems and connect the suppression of QME under spatial inhomogeneity to localization phenomena in tilted and disordered Bose--Hubbard chains.

cond-mat.quant-gas↗
arXiv2026

Quantum Inspired Vehicular Network Optimization for Intelligent Decision Making in Smart Cities

Connected and automated vehicles require city-scale coordination under strict latency and reliability constraints. However, many existing approaches optimize communication and mobility separately, which can degrade performance during network outages and under compute contention. This paper presents QIVNOM, a quantum-inspired framework that jointly optimizes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication together with urban traffic control on classical edge--cloud hardware, without requiring a quantum processor. QIVNOM encodes candidate routing--signal plans as probabilistic superpositions and updates them using sphere-projected gradients with annealed sampling to minimize a regularized objective. An entanglement-style regularizer couples networking and mobility decisions, while Tchebycheff multi-objective scalarization with feasibility projection enforces constraints on latency and reliability. The proposed framework is evaluated in METR-LA--calibrated SUMO--OMNeT++/Veins simulations over a $5\times5$~km urban map with IEEE 802.11p and 5G NR sidelink. Results show that QIVNOM reduces mean end-to-end latency to 57.3~ms, approximately $20\%$ lower than the best baseline. Under incident conditions, latency decreases from 79~ms to 62~ms ($-21.5\%$), while under roadside unit (RSU) outages, it decreases from 86~ms to 67~ms ($-22.1\%$). Packet delivery reaches $96.7\%$ (an improvement of $+2.3$ percentage points), and reliability remains $96.7\%$ overall, including $96.8\%$ under RSU outages versus $94.1\%$ for the baseline. In corridor-closure scenarios, travel performance also improves, with average travel time reduced to 12.8~min and congestion lowered to $33\%$, compared with 14.5~min and $37\%$ for the baseline.

cs.NI↗
arXiv2026

Challenge-Response Quantum Reinforcement Learning with Application to Quantum-Assisted Authentication

Quantum reinforcement learning (QRL) has emerged as a promising research direction that integrates quantum information processing into reinforcement learning frameworks. While many existing QRL studies apply quantum agents to classical environments, it has been realized that the potential advantages of QRL are most naturally explored in environments that exhibit intrinsically quantum characteristics, where the agent's observations and interactions arise from quantum processes. In this work, we propose a quantum reinforcement learning environment formulated as a challenge-response task with hidden information. In the proposed environment, Alice encodes a classical bit into the parameters of a quantum circuit, while Bob, with a trained reinforcement learning agent, interacts with a limited number of quantum state copies to infer the hidden bit. The agent must select measurement strategies and decide when to terminate the interaction under explicit resource constraints. To study the solvability of the proposed environment, we consider three agents: a purely classical agent, a lightweight hybrid agent and a deep hybrid agent. Through experiments, we analyze the trade-off between inference accuracy and quantum resource consumption under varying interaction penalties. Our results show that the lightweight hybrid agent achieves reliable inference using as few as two quantum state copies, outperforming both the classical baseline and the deep hybrid agent across both high and low-penalty regimes. We further evaluate robustness under realistic quantum noise models and discuss the relevance of the proposed environment for security-oriented applications, including quantum-assisted authentication.

quant-ph↗
arXiv2026

Resource Constraint Estimation of In-Quantum Implementated Mini-AES

The advancement in quantum technology has brought the implementation of Grover's Search attack on cybersecurity algorithms much closer to reality. For such tasks, the Hilbert space has to be designed to make the relation between the input and output of these cryptographic algorithms in quantum circuits. Also, these algorithms are tested on current quantum simulators such as Qiskit, where memory constraints and limited processing power are hurdles. Here, we present an easy-to-implement method using a conventional arithmetic number field approach which can be applied to quantum circuits by CNOT and Toffoli gates, while focusing on resource constraints. Also, we give a Python code that can generate a Qiskit code for the quantum implementation of similar cryptographic S-boxes using the CNOT and 3-Tofflie gates by using simple logic presented in this paper.

quant-ph↗
arXiv2026

Machine Learning Techniques for Enhancing Quantum Key Distribution

Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.

quant-ph↗