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Quantum-Secured DSP-Lite Data Transmission Architectures for AI-Driven Data Centres

Quantum Physics 2025-10-21 v1

Abstract

Artificial intelligence-driven (AI-driven) data centres, 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. Here, we propose and demonstrate a quantum-secured digital signal processing-lite (DSP-Lite) data transmission architecture that meets all the stringent requirements for AI-driven data centre optical interconnects (AI-DCIs) scenarios. By integrating a self-homodyne coherent (SHC) system and quantum key distribution (QKD) through the multicore-fibre-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) and a quantum secret key rate (SKR) of 229.2 kb/s, with a quantum bit error rate (QBER) of approximately 1.27% and with ultralow power consumption. Our work paves the way for constructing secure, scalable, and cost-efficient data transmission frameworks, thus enabling the next generation of intelligent, leak-proof optical interconnects for data centres.

Keywords

Cite

@article{arxiv.2503.09940,
  title  = {Quantum-Secured DSP-Lite Data Transmission Architectures for AI-Driven Data Centres},
  author = {Xitao Ji and Wenjie He and Junda Chen and Mingming Zhang and Yuqi Li and Ziwen Zhou and Zhuoxuan Song and Hao Wu and Siqi Yan and Kejin Wei and Zhenrong Zhang and Shuang Wang and Ming Tang},
  journal= {arXiv preprint arXiv:2503.09940},
  year   = {2025}
}
R2 v1 2026-06-28T22:18:25.463Z