Quantum key distribution (QKD) is a secure communication technique that uses quantum mechanical principles. This method enables two trusted users to exchange a secret key or message securely by exploiting the fundamental properties of quantum mechanics. Strong long-distance secure communication is provided by QKD but its effectiveness is mainly limited by quantum bit error rate (QBER), finite-key effects, and the effectiveness of classical error-correction during information reconciliation. In this work, we proposes a neural-aided adaptive reconciliation (NAAR) framework based on artificial neural networks for QKD that integrates a lightweight neural belief propagation decoder with QBER-driven adaptive LDPC coderate selection. By lowering frame-error rate and information leakage, neural-assisted log-likelihood ratio calibration enhances belief-propagation convergence for binary symmetric channels. The result shows an improvement in secret key rates (SKR) and lower error rates over traditional LDPC-based reconciliation.
Quantum key distribution (QKD) is a secure communication technique that uses quantum mechanical principles. This method enables two trusted users to exchange a secret key or message securely by exploiting the fundamental properties of quantum mechanics. Strong long-distance secure communication is provided by QKD but its effectiveness is mainly limited by quantum bit error rate (QBER), finite-key effects, and the effectiveness of classical error-correction during information reconciliation. In this work, we proposes a neural-aided adaptive reconciliation (NAAR) framework based on artificial neural networks for QKD that integrates a lightweight neural belief propagation decoder with QBER-driven adaptive LDPC coderate selection. By lowering frame-error rate and information leakage, neural-assisted log-likelihood ratio calibration enhances belief-propagation convergence for binary symmetric channels. The result shows an improvement in secret key rates (SKR) and lower error rates over traditional LDPC-based reconciliation.
การกระจายกุญแจรหัสลับเชิงควอนตัมถือเป็นหนึ่งในเทคโนโลยีความปลอดภัยด้านการสื่อสาร โดยอาศัยหลักกลศาสตร์ควอนตัมเพื่อกำเนิดและแลกเปลี่ยนข้อมูลกุญแจรหัสลับระหว่างคู่สื่อสารให้มีความปลอดภัยสูงสุดสำหรับระบบวิทยาการรหัสลับ ซึ่งต้องอาศัยกระบวนการไกล่เกลี่ยความผิดพลาดเพื่อจุดประสงค์ในการยืนยันความถูกต้องของข้อมูลกุญแจรหัสลับระหว่างคู่สื่อสารให้มีค่าที่ตรงกัน ในบทความนี้ นำเสนอวิธีการประมาณค่าอัตราความผิดพลาดของช่องสัญญาณเชิงควอนตัมบนพื้นฐานของการประมาณค่าภาวะน่าจะเป็นสูงสุดจากชุดข้อมูลซินโดรมในระหว่างขั้นตอนการไกล่เกลี่ยความผิดพลาดสำหรับการปรับค่าอัตราการเข้ารหัสให้เหมาะสมกับความผิดพลาดของบิตข้อมูลกุญแจเชิงควอนตัมที่เกิดขึ้นในแต่ละช่วงเวลาด้วยรหัสตรวจสอบพาริตี้แบบความหนาแน่นต่ำ ตลอดจนการนำมาประยุกต์ใช้แทนขั้นตอนการเปิดเผยบิตข้อมูลกุญแจบางส่วนที่สูญเสียไปในการประมาณค่าความผิดพลาดแบบดั้งเดิม นำไปสู่ผลลัพธ์ของขนาดบิตข้อมูลกุญแจที่เพิ่มสูงขึ้น โดยจากการวิเคราะห์และทดสอบผลพบว่า วิธีการที่นำเสนอให้ความแม่นยำในการประมาณค่าอัตราความผิดพลาดครอบคลุมเงื่อนไขของการกระจายกุญแจรหัสลับเชิงควอนตัม นำไปสู่การเพิ่มประสิทธิภาพการไกล่เกลี่ยความผิดพลาดที่สูงขึ้นทั้งในด้านความปลอดภัยและอัตราการกำเนิดกุญแจรหัสลับ สนับสนุนการประยุกต์ใช้งานจริงในระบบการกระจายกุญแจรหัสลับเชิงควอนตัมความเร็วสูงQuantum Key Distribution (QKD), one of the communication security technologies, employs the properties of quantum mechanics to guarantee and exchange the perfectly-secured key generation between two legitimate parties for cryptographic purposes. The reconciliation process is required to correct the transmission error after the distribution of quantum objects. In this paper, the channel parameter estimation is proposed to estimate the error rate in the quantum channel by using the Maximum-Likelihood Estimation (MLE) based on the syndrome information for rate-adaptive reconciliation. The specific Low-Density Parity-Check (LDPC) codes are adapted to optimize their coding rates responding to the possible cases of error in QKD. The proposed method can be applied to avoid wasting sample keys in the traditional channel estimation, and led to increase the final secret key length. The simulation results show an accuracy of the estimated quantum bit error rate (QBER) for covering the range in QKD system. The gain of rate-adaptive reconciliation based on MLE confirms an improvement in the reconciliation efficiency that significantly impacts on the achievable secret key generation rate for high QKD throughput applications.
The purpose of reconciliation, one of classical aspects of quantum key distribution (QKD) protocol, is to mitigate errors after the distribution of quantum information over a quantum channel. In this paper, a rate-adaptive method is proposed by means of a channel coding scheme where specific low-density parity-check (LDPC) codes are adapted in the Slepian-Wolf coding system. Puncturing and shortening techniques are adopted to optimize their coding rates with all possible cases of error in QKD. This successful method utilizes the knowledge of the quantum bit error rate (QBER) based on the maximum-likelihood estimator with respect to the syndrome information. Its benefit is that avoids wasting sample keys in the traditional channel estimation and thus increases the size of the final secret key. Evidently, simulation results confirm an improvement in the reconciliation efficiency with minimal interaction that suitable for high QKD throughput applications.
In continuous-variable quantum key distribution (CV-QKD) systems, information reconciliation (IR) is a crucial step that significantly affects the secret key rate (SKR). The fixed-rate error-correcting codes used in IR are highly sensitive to changes in the signal-to-noise ratio (SNR) and cannot maintain a high reconciliation efficiency in practical CV-QKD systems. To address this issue, we first propose a rate-adaptive IR protocol, namely Threshold-based IR (TIR), which changes the code rate of low-density parity-check (LDPC) codes by selectively revealing bits with lower reliability and adjusting their log-likelihood ratios (LLRs). Then, we propose a rate-adaptive IR protocol, namely Sorting-based IR (SIR), which not only adjusts the code rate according to variations in SNR, but also enables the CV-QKD systems to achieve high reconciliation efficiency over a wide range of SNRs. Furthermore, we perform an analysis of the protocols in terms of code rate, reconciliation efficiency, and complexity. The simulation results demonstrate that the proposed protocols outperform other rate-adaptive IR protocols, achieving a reconciliation efficiency higher than 98.5% in the SNR range below -20 dB and maintaining a certain SKR in long-distance transmission.
Quantum Key Distribution (QKD) enables information-theoretic secure key exchange by exploiting fundamental quantum phenomena such as superposition and no-cloning. Classical QKD protocols (e.g., BB84) guarantee that any eavesdropping attempt introduces detectable disturbances, but they typically assume stable, high-quality channels and unconstrained devices. In contrast, Internet of Things (IoT) networks present a radically different environment: devices have limited processing power, minimal onboard memory, strict energy budgets, and communicate over highly variable wireless links subject to interference and rapid fading. These constraints lead to elevated quantum bit error rates (QBER), frequent key reconciliation failures, and prohibitive energy costs, all of which jeopardize practical QKD deployment. To address these challenges, we propose an AI-powered QKD protocol specifically optimized for IoT scenarios. Our approach integrates three AI modules: (1) a gradient-boosted regression channel estimator that predicts instantaneous link transmittance using lightweight sensor data and historical photon counts; (2) a reinforcement learning (RL) agent that adaptively tunes photon intensity, basis-selection probability, and reconciliation block size to balance key rate and error rate; and (3) a convolutional neural network (CNN) classifier that selects the optimal Low-Density Parity-Check (LDPC) code rate based on real-time noise characteristics. We simulate a star-topology IoT network of 50 battery-powered devices communicating over 5 km polarization-encoded links with realistic urban loss (0.1–0.3 dB/km) and environmental noise. Compared against a static BB84 baseline, our AI-enhanced protocol reduces average QBER from 4.5% to 2.9%, increases secure key rate from 12.5 kbps to 16.0 kbps, lowers latency by 22%, and reduces energy consumption per key bit by 18%. These improvements persist across channel conditions and device heterogeneity, demonstrating robustness.
Quantum key distribution (QKD) enables unconditional secure communication by exploiting the principles of quantum mechanics. However, existing reconciliation protocols face a trade-off between error correction efficiency and information leakage. To address this challenge, we propose an enhanced QKD reconciliation scheme based on polynomial interpolation. The protocol introduces a block-wise algebraic structure to unify error detection and correction, significantly reducing interactive rounds while maintaining high efficiency. A comprehensive information-theoretic leakage model is established, covering polynomial interpolation, verification, and iterative error localization. To further ensure unconditional security, a cascaded privacy amplification mechanism is integrated, combining two compression stages tailored to reconciliation leakage and quantum channel leakage, respectively. Moreover, an adaptive parameter optimization strategy dynamically adjusts block size and polynomial order according to the quantum bit error rate (QBER), achieving robustness under diverse channel conditions. Theoretical analysis and simulations demonstrate that the proposed scheme achieves a secure key rate of 65–72% at QBER < 3%, outperforming the classical CASCADE protocol by 8–12%, and maintains 30–50% efficiency under moderate error rates. Computational complexity analysis shows performance comparable to LDPC-based approaches while offering superior leakage control. These results confirm that the proposed protocol provides a balanced trade-off among efficiency, security, and implementation complexity, making it suitable for deployment in practical QKD systems.
The postprocessing or secret-key distillation process in quantum key distribution (QKD) mainly involves two well-known procedures: information reconciliation and privacy amplification. Information or key reconciliation has been customarily studied in terms of efficiency. During this, some information needs to be disclosed for reconciling discrepancies in the exchanged keys. The leakage of information is lower bounded by a theoretical limit, and is usually parameterized by the reconciliation efficiency (or inefficiency), i.e. the ratio of additional information disclosed over the Shannon limit. Most techniques for reconciling errors in QKD try to optimize this parameter. For instance, the well-known Cascade (probably the most widely used procedure for reconciling errors in QKD) was recently shown to have an average efficiency of 1.05 at the cost of a high interactivity (number of exchanged messages). Modern coding techniques, such as rate-adaptive low-density parity-check (LDPC) codes were also shown to achieve similar efficiency values exchanging only one message, or even better values with few interactivity and shorter block-length codes.