Artificial intelligence-driven (AI-driven) data centers, which require high-performance, scalable, energy-efficient, and secure infrastructure, have led to unprecedented data traffic demands. These demands involve low-latency, high-bandwidth connections, low power consumption, and data confidentiality. However, conventional optical interconnect solutions, such as intensity-modulated direct detection and traditional coherent systems, cannot address these requirements simultaneously. In particular, conventional encryption protocols that rely on complex algorithms are increasingly vulnerable to the rapid advancement of quantum computing. We propose and demonstrate a quantum-secured data transmission architecture that involves minimal digital signal processing (DSP) consumption and meets all the stringent requirements for AI-driven data center optical interconnect (AI-DCI) scenarios. By integrating a self-homodyne coherent (SHC) system and quantum key distribution (QKD) through the multicore-fiber-based space division multiplexing (SDM) technology, our scheme enables secure, high-capacity, and energy-efficient data transmission while ensuring resilience against quantum computing threats. In our demonstration, we achieved an expandable transmission capacity of 2 Tbit per second (Tb/s) with a quantum secret key rate (SKR) of 229.2 kb/s and further validated real-time encrypted transmission using AES-256 encryption with QKD-generated keys. Our work paves the way for constructing secure, scalable, and cost-efficient data transmission frameworks, enabling the next generation of intelligent, leak-proof optical interconnects for data centers.
Artificial intelligence-driven (AI-driven) data centers, which require high-performance, scalable, energy-efficient, and secure infrastructure, have led to unprecedented data traffic demands. These demands involve low-latency, high-bandwidth connections, low power consumption, and data confidentiality. However, conventional optical interconnect solutions, such as intensity-modulated direct detection and traditional coherent systems, cannot address these requirements simultaneously. In particular, conventional encryption protocols that rely on complex algorithms are increasingly vulnerable to the rapid advancement of quantum computing. We propose and demonstrate a quantum-secured data transmission architecture that involves minimal digital signal processing (DSP) consumption and meets all the stringent requirements for AI-driven data center optical interconnect (AI-DCI) scenarios. By integrating a self-homodyne coherent (SHC) system and quantum key distribution (QKD) through the multicore-fiber-based space division multiplexing (SDM) technology, our scheme enables secure, high-capacity, and energy-efficient data transmission while ensuring resilience against quantum computing threats. In our demonstration, we achieved an expandable transmission capacity of 2 Tbit per second (Tb/s) with a quantum secret key rate (SKR) of 229.2 kb/s and further validated real-time encrypted transmission using AES-256 encryption with QKD-generated keys. Our work paves the way for constructing secure, scalable, and cost-efficient data transmission frameworks, enabling the next generation of intelligent, leak-proof optical interconnects for data centers.
Amidst the development of communication technologies, the limitations of 5G communication systems, particularly in security and resource management, have become increasingly apparent. As the 6G era approaches, there are growing demands for integrated systems that ensure robust security and optimize resource utilization. To address these challenges, this paper proposes a novel architecture that integrates classical communication channels and quantum key distribution (QKD) channels using wavelength-division multiplexing (WDM). This innovative approach allows classical and quantum channels to coexist within a single optical fiber, improving security and fiber utilization while reducing system complexity and cost. A key contribution of this work is introducing an AI-based prediction and management module, dynamically adjusting the quantum key generation rate according to real-time usage and forecasted demand. By leveraging machine learning, the module predicts quantum key consumption patterns, ensuring that the key generation rate aligns with the system’s security and performance requirements. This approach balances key generation and usage, minimizing resource waste and enhancing the system’s overall efficiency. The feasibility of the proposed architecture is validated through an analysis of spontaneous Raman scattering (SpRS), showing the architecture maintains high key generation rates and low quantum bit error rates (QBER) under optimal system conditions. Furthermore, an analysis reveals that the AI module meets the requirement of predicting key usage and managing the key generation rate. Lastly, the feasibility is further validated by an experiment. Overall, this study presents a comprehensive framework for integrating classical and quantum channels in future 6G networks, providing enhanced security, efficiency, and scalability.
The advancement of 6G networks demands high-capacity, ultra-low latency, and secure communication infrastructure. Traditional fronthaul solutions face significant challenges in meeting these demands due to increasing data traffic, network densification, and security vulnerabilities. To address these issues, this paper proposes an innovative 6G passive Wavelength Division Multiplexing (WDM) fronthaul architecture integrated with Quantum Key Distribution (QKD). By leveraging quantum cryptographic principles, our approach ensures unbreakable security and protects network transmissions from potential cyber threats and eavesdropping attacks. The proposed architecture effectively combines high-speed optical transmission with quantum-secured key exchange, optimizing network reliability and reducing security risks. Through comprehensive simulations, we evaluate key performance factors, including secure key rate (SKR), quantum bit error rate (QBER), detector efficiency, and quantum transmission loss. Results indicate that our architecture maintains high SKR with minimal QBER, even under challenging transmission conditions, ensuring robust quantum-secured communication. Additionally, the passive nature of WDM significantly reduces power consumption and maintenance costs, improving network sustainability. This research establishes a practical pathway for integrating quantum security into next-generation optical fronthaul networks, enabling highly secure, scalable, and efficient data transmission for future 6G applications, including autonomous vehicles, smart cities, and industrial IoT.
Artificial Intelligence (AI) combined with Bio-Quantum Computing is a revolutionary method of improving data security and encryption in virtual environments. Through the use of quantum mechanics and AI-optimized optimization, this combination strengthens cryptographic methods against future cyber threats. Current encryption algorithms like RSA and AES are vulnerable due to the advancement of Bio-Quantum Computing, which can decrypt conventional cryptographic algorithms. Additionally, traditional security systems are not able to adapt in real-time to advanced cyber-attacks. To meet these demands, the suggested framework combines AI with Quantum Key Distribution (AI -QKD). The QKD provides unbreakable key exchange cryptography using quantum properties, whereas AI optimizes encryption protocols in real time through threat analysis, providing heightened security resilience. This technique is used in virtual world transaction security, identity authentication, and secret communications. The AI-based mechanism flexibly adjusts encryption parameters in real time, preventing quantum attacks and increasing the integrity of the overall data. Findings reveal that the new approach enhances the robustness of encryption immensely, lowers risks of key compromise, and makes virtual interactions secure. The fusion of AI and QKD reflects high adaptability, making the encryption systems quantum-resistant and future-proof. The new approach achieves the data integrity by 98.70%, key compromise risks by 45.31%, adaptability by 97.63%.
Next generation communication networks require advanced solutions in security to prevent the complex increasing threats in cyber space. This research combines quantum cryptography and artificial intelligence (AI) as a way to make network security stronger by protecting data’s confidentiality, integrity and authentication. Four key algorithms – Quantum Key Distribution (QKD), AI based Intrusion Detection, Blockchain enhanced Encryption and Post Quantum Cryptography are studied in the paper to assess their capability to secure the communication infrastructures.
Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I2C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes.
Secure and efficient computing of AI is highly essential in modern defense and cloud infrastructure, for which traditional electronic architecture often faces performance and security bottlenecks. This paper presents an integrated framework that incorporates QKD with photonic neural networks to realize quantum-secured ultrafast AI computation. The integrated framework establishes QKD-secured channels for securing AI data transmission while using optical neural networks for high-speed inference and low-energy processing. Experimental simulation confirms the integrated approach attains as high as 40% latency reduction and 35% energy saving when compared with state-of-the-art conventional AI systems secured by classical cryptography. Moreover, the integrated QKD enables provably secure key exchange resistant against both classical and quantum attacks. This work thus points out the potential of quantum-photonic integration as a base for scalable, secure, and high-efficiency AI computing both in defense and cloud applications.