arXiv subjects

Noureldin Mohamed

Publications and source records attributed to Noureldin Mohamed.

5 recordsLinked to original sources

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

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

Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution

While Quantum Key Distribution (QKD) provides information-theoretic security, the transition from theory to physical hardware introduces side-channel vulnerabilities that traditional error metrics often fail to characterize. This paper presents a high-fidelity simulation framework for intrusion detection in decoy-state QKD, modeled as a minimax game between a learning-based defender and a physically constrained, adaptive adversary. The defender utilizes block-level telemetry (comprising decoy-state residuals, timing-histogram moments, and detector imbalances) to trigger alarms that gate key distillation . Unlike heuristic thresholds, our optimization objective is strictly operational: missed detections are penalized based on the resulting degradation of the finite-key secret fraction calculated via three-intensity decoy estimators and entropy-accumulation (EAT) penalties. The emulated adversary performs an automated search over time-shift, detector-blinding, photon number splitting (PNS), and Trojan-horse families, subject to hardware-limited feasibility bands. Concurrently, the defender co-trains one-class and temporal detectors (LSTM/TCN) using hard-negative mining to minimize the missed-attack rate at a calibrated false-alarm rate ($\text{FAR}$). Under adaptive attack scenarios, the system preserves $82\text{--}92\%$ of the honest finite-key rate while discarding only approximately $1.2\%$ of traffic, representing a net gain of $+20\text{--}35$ percentage points in usable secret bits over non-adversarial baselines. These results demonstrate that optimizing detection directly for secret-bit retention provides a robust, physically grounded layer of defense against adaptive side-channel strategies in practical QKD deployments.

quant-ph↗

arXiv2026

EAQKD: Entanglement-Based Authenticated Quantum Key Distribution

The promise of unconditional security in the Quantum Key Distribution (QKD) depends on the availability of an authenticated classical channel. However, practical implementations often overlook this requirement or rely on computational assumptions that compromise long-term security. To overcome these challenges, this paper presents Entanglement-Based Authenticated Quantum Key Distribution (EAQKD), a novel protocol that addresses critical security and practical limitations in quantum cryptographic key exchange. Our approach integrates quantum entanglement distribution with information-theoretic authentication. We evaluate EAQKD's performance through a comprehensive discrete-event simulation framework modeled on realistic channel characteristics and experimental device parameters. Our modeling incorporates parameters from practical quantum optics setups, including SPDC entanglement sources, superconducting nanowire detectors, and fiber channel imperfections. Our results show quantum bit error rates consistently below the 11% security threshold (ranging from 1.86% at 10 km to 9.27% at 200 km), with secure key rates achieving $1.12 \times 10^5$ bits/s at short distances and maintaining practical rates of 9.8 bits/s at 200 km. When integrated with quantum repeater architectures, our analysis projects that EAQKD can extend secure communication beyond 500 km while providing information-theoretic security guarantees. Comparative analysis against the BB84, E91, and Twin-Field QKD protocols demonstrates EAQKD's superior balance of security, practical performance, and implementation robustness. This work advances quantum cryptography by providing a rigorously analyzed engineering reference for secure key distribution in future quantum communication networks.

quant-ph↗

arXiv2026

Resisting Quantum Key Distribution Attacks Using Quantum Machine Learning

The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public-key cryptographic algorithms. Quantum Key Distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as Photon Number Splitting and Trojan-Horse attacks. In this work, we investigate the potential of quantum machine learning (QML) to detect QKD attacks. In particular, we propose a Hybrid Quantum Long Short-Term Memory (QLSTM) model to improve detection performance. By combining quantum-enhanced learning with classical deep learning, the model captures temporal patterns in QKD data, improving detection accuracy. To evaluate the proposed model, we introduce a QKD dataset that simulates typical operations along with multiple attack scenarios, including Intercept-and-Resend, Photon-Number Splitting, Trojan-Horse, Detector Blinding, and Combined attacks. The dataset includes Quantum Bit Error Rate (QBER), signal and decoy loss rates, and time-based metrics. Our results demonstrate the promising performance of the quantum machine learning approach compared to classical models. The proposed Hybrid QLSTM achieved an accuracy of 94.7% after 50 training epochs. The evaluation is conducted on a semi-realistic, simulation-generated decoy-state BB84 dataset, and the reported performance should be interpreted as a proof-of-concept rather than a final assessment on field-deployed QKD systems.

cs.CR↗