Quantum Connected Collaborative Learning with Superdense Coding for Wireless Internet-of-Everything Networks
Goki F. R. Purba, Bhaskara Narottama, Saeed R. Khosravirad +2 more
In recent years, distributed wireless communication optimization, where training data is stored remotely from local multi-access edge computing (MEC) processors to preserve data security privacy and minimize complexity, has seen noteworthy progress for relevant wireless Internet-of-Everything (WIoE) networks beyond 6G. Nonetheless, the exploding number of WIoE clients requires secure data storage and scaled data processing at the network and transmitter, which local processors might be unable to afford. Parallel to this, we are witnessing widespread quantum-enabled learning adoptions for optimizing wireless communications. The rapid growth of quantum technologies has introduced security concerns for classical channels, due to their potential to undermine classical cryptographic approaches. This paper, therefore, considers the adoption of quantum-enabled learning with quantum communication protocol, especially quantum secure direct communication (QSDC) via superdense coding. While the processing learning happens across different locations for next-generation WIoE networks, the QSDC prevents vulnerabilities of data poisoning and model stealing in connected quantum collaborative learning.
Asilomar Conference on Signals, Systems and Computers (ACSSC)•Oct 26, 2025•0 citations