Description ΩQKD introduces a structure-sensitive operational security monitoring framework for Quantum Key Distribution (QKD) systems. Instead of relying solely on the Quantum Bit Error Rate (QBER), the proposed methodology integrates multiple normalized operational indicators—including QBER, secret key rate deficit, photon loss, timing drift, detector noise, and channel instability—into a unified mathematical security index. The framework establishes a rigorous mathematical foundation through boundedness, monotonicity, Lipschitz stability, generalized power-mean functionals, and a risk-entropy metric for distinguishing distributed degradation from structured attacks. Objective parameter weighting is performed using Shannon entropy (CRITIC-type) weighting, while alarm thresholds are determined using statistically controlled false-alarm criteria. Numerical validation based on 12,000 Monte Carlo scenarios demonstrates that ΩQKD significantly improves operational anomaly detection compared with conventional QBER-only monitoring, particularly under stealthy attack scenarios where individual indicators may remain within nominal limits. This work does not propose a new QKD protocol or a new information-theoretic security proof. Instead, it introduces a mathematically grounded operational monitoring and security evaluation methodology intended to complement existing QKD security analyses and support real-time health assessment of practical quantum communication systems.
Description ΩQKD introduces a structure-sensitive operational security monitoring framework for Quantum Key Distribution (QKD) systems. Instead of relying solely on the Quantum Bit Error Rate (QBER), the proposed methodology integrates multiple normalized operational indicators—including QBER, secret key rate deficit, photon loss, timing drift, detector noise, and channel instability—into a unified mathematical security index. The framework establishes a rigorous mathematical foundation through boundedness, monotonicity, Lipschitz stability, generalized power-mean functionals, and a risk-entropy metric for distinguishing distributed degradation from structured attacks. Objective parameter weighting is performed using Shannon entropy (CRITIC-type) weighting, while alarm thresholds are determined using statistically controlled false-alarm criteria. Numerical validation based on 12,000 Monte Carlo scenarios demonstrates that ΩQKD significantly improves operational anomaly detection compared with conventional QBER-only monitoring, particularly under stealthy attack scenarios where individual indicators may remain within nominal limits. This work does not propose a new QKD protocol or a new information-theoretic security proof. Instead, it introduces a mathematically grounded operational monitoring and security evaluation methodology intended to complement existing QKD security analyses and support real-time health assessment of practical quantum communication systems.
Q-ERUM (Quantum Early-warning Reliability Under Monitoring) is a structure-sensitive temporal monitoring framework for Quantum Key Distribution (QKD) systems. Unlike conventional monitoring approaches that generate alarms only after a critical threshold has been crossed, Q-ERUM is designed to detect progressive operational degradation before the communication link reaches an unacceptable reliability level. The framework operates on the instantaneous reliability-risk functional introduced by the companion ΩQKD methodology and extends it with temporal analysis through risk velocity, acceleration, baseline anomaly detection, and a false-alarm-controlled decision engine. It provides mathematically grounded early-warning capabilities supported by boundedness, monotonicity, additive drift accumulation, Lipschitz stability, risk-entropy analysis, and lead-time estimation under linear extrapolation. Numerical validation includes Monte Carlo simulations with up to 100,000 synthetic QKD time-series episodes, demonstrating substantial improvements in early-warning performance compared with threshold-only monitoring and QBER-only anomaly detection. Additional analyses evaluate parameter sensitivity, measurement-noise robustness, and transferability across representative BB84, Decoy-State, MDI-QKD, and Twin-Field QKD operational profiles. Q-ERUM does not introduce a new QKD protocol or a new information-theoretic security proof. Instead, it provides a mathematically rigorous operational early-warning and trend-monitoring methodology intended to complement existing QKD security analyses by continuously assessing the reliability evolution of practical quantum communication systems.
Abstract I present a physical-layer monitoring framework for quantum key distribution (QKD) implementations based on Krylov/operator-growth diagnostics. Running alongside standard parameter estimation, privacy amplification, and composable security proofs, it addresses a focused question: whether the temporal correlation structure of an observed quantum bit error rate (QBER) stream carries additional information about physical channel perturbations. Within a specified local-Hamiltonian channel model, the Lanczos coefficients of the channel Liouvillian define an operator-growth autocorrelation template, and deviations from it serve as anomaly scores for implementation monitoring. For the model studied here, the QBER autocorrelation relates to the operator autocorrelation through a proportionality relation, CQBER(τ)=α(N)Cop(τ) C QBER ( τ ) = α ( N ) C op ( τ ) , with α(N) α ( N ) an explicitly specified calibration factor. Local Hamiltonian perturbations generically distort the associated Krylov fingerprint once the perturbation reaches the support of the monitored observable, consistent with the locality of the Lanczos recursion and with Lieb–Robinson bounds. The resulting three-layer pipeline combines adaptive frequency filtering, Krylov-template extraction, and calibrated slope detection. On simulated BB84-like QBER data with realistic hardware noise, the detector achieves high anomaly discrimination under the tested perturbation classes ( AUC=0.9899 AUC = 0.9899 ). On 181,606 181 , 606 public QBER measurements from a deployed fiber-optic QKD system, it yields a 4.5% clean-test alarm rate against a 5% design target and detects injected perturbations with 96.0% sensitivity at ΔQBER=+0.5% Δ QBER = + 0.5 % ( AUC=0.981 AUC = 0.981 ). Certification of final key secrecy remains governed by the underlying QKD protocol, parameter estimation, finite-key analysis, and privacy amplification.
Quantum Key Distribution (QKD) is increasingly recognized as a robust solution to the vulnerabilities posed by quantum computing to classical encryption systems. This paper presents a comprehensive investigation of the BB84 protocol, the foundational QKD scheme, through simulated quantum communication using IBM’s Qiskit framework. The study models quantum key exchange under both ideal and adversarial conditions, including channel noise and intercept-resend attacks. The Quantum Bit Error Rate (QBER) was measured as a function of noise intensity and eavesdropping, confirming that QBER increases predictably in compromised channels. A threshold of 11% QBER was used to determine the viability of secure key generation. Final key retention rates were analyzed across multiple channel scenarios, revealing BB84’s sensitivity to both photon loss and adversarial interference.\n\nSecurity vulnerabilities such as photon-number-splitting and detector blinding were assessed, and countermeasures including decoy-state protocols and authenticated classical channels were proposed. Four key performance graphs were generated to visualize system behavior under realistic operational conditions. The results support BB84’s theoretical advantage in ensuring information-theoretic security while acknowledging real-world limitations in transmission range and hardware reliability. The paper concludes by addressing future improvements in QKD systems, including satellite-based distribution, quantum repeaters, and device-independent implementations. Through simulation-driven analysis, this study validates BB84 as a practical defense against quantum-enabled attacks and underscores the need for continued development and standardization in post-quantum cryptographic infrastructure.
Quantum communication protocols offer theoretically unbreakable security by leveraging the fundamental principles of quantum mechanics.Among them, the DL04 protocol enables direct, deterministic secure communication without requiring a pre-shared key.However, practical implementations remain vulnerable to performance degradation due to channel losses, detector inefficiencies, and increased QBER, which may compromise security.This paper presents a machine learningbased framework for predicting operational failures and assessing security risks in DL04 QSDC systems.A dataset of 48 simulated scenarios was generated, varying key physical parameters such as attenuation, depolarization, and detector efficiency over a range of transmission distances.Using regression models-including ensemble methods such as Gradient Boosting and Random Forest-we achieved highly accurate predictions of secure key rate and QBER.These models enable real-time monitoring, anomaly detection, and dynamic adjustment of protocol parameters, enhancing both performance and security.Results demonstrate that machine learning can effectively anticipate system behavior under different conditions, providing a foundation for intelligent, adaptive QKD systems.This approach represents a significant step toward resilient quantum communication architectures, particularly under realistic noise and hardware constraints.
This thesis is concerned with rigorous security analyses of practical Quantum Key Distribution (QKD) protocols, using a variety of modern proof techniques. The main results are as follows. First, we establish a security proof for variable-length QKD protocols against IID collective attacks, and extend this result to coherent attacks using the postselection technique. In doing so, we resolve a long-standing flaw in the application of the postselection technique to QKD, thereby placing it on a rigorous mathematical footing. Second, we develop a method to bound phase error rates in entropic uncertainty relation-based and phase error rate-based proofs, using only the observed statistics of the protocol, even when detectors are imperfect and only approximately characterized. This removes a key assumption of identical detector behaviour and enables these techniques to be applied in realistic settings. Third, we present a very general security analysis based on the marginal-constrained entropy accumulation theorem. The resulting framework can be readily adapted to practical imperfections and side channels, and is suitable for certification efforts. Finally, we show that the security of QKD protocols under realistic authentication assumptions can be reduced to the standard idealized setting, where authentication is assumed to behave honestly, with only minor protocol modifications. A distinctive feature of this thesis is its unified presentation of several major QKD security proof frameworks using consistent protocol descriptions and notation. Consequently, this thesis is intended not only as a collection of new technical results, but also as a useful reference for understanding rigorous security analysis in quantum key distribution.
Description Q-HKM (Quantum Hybrid Key Manager) is a reliability-aware operational decision framework for hybrid Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) systems. The framework addresses a fundamental operational question in quantum-safe communications: how to dynamically allocate reliance between QKD-derived and PQC-derived key material according to their current operational availability while preserving the security guarantees of hybrid cryptography. Unlike approaches that combine security levels through weighted averaging, Q-HKM explicitly separates security from operational availability. Hybrid security is provided by a standard hybrid KEM combiner, ensuring that the combined key remains secure whenever at least one underlying key source remains secure. The proposed weighting mechanism is applied only to operational availability and quality-of-service, avoiding the conceptual inconsistency of averaging cryptographic security levels. The framework introduces a mathematically grounded hybrid availability functional, availability-tracking allocation, hysteresis-based switching stability, multi-source generalization, and margin-based early-warning analysis. Theoretical properties include boundedness, convex enclosure, risk-availability equivalence, allocation stability, and combiner-independent security composability. Numerical validation is performed using 16,000 synthetic hybrid PQC–QKD operational scenarios, evaluating delivered availability, outage probability, source-switching stability, and early-warning capability under multiple operational conditions, including QKD degradation, PQC deprecation, and correlated failures. Q-HKM does not introduce a new cryptographic primitive, QKD protocol, PQC algorithm, or security proof. Instead, it provides a mathematically rigorous operational decision layer that complements existing hybrid cryptographic architectures by optimizing key-source allocation according to real-time operational availability while preserving established hybrid security guarantees.