Terahertz (THz) communication is a key enabling technology for achieving high-capacity, long-distance inter-satellite and satellite-ground communications in the next-generation wireless systems. Photonic-assisted upconversion provides a cost-effective approach to realizing ultra-wideband THz communication systems. To explore the potential of quantum neural network for the photonic-assisted THz communication systems, this work is the first to design the hybrid quantum-classical neural network for quadrature amplitude modulation (QAM) symbol recovery over tens of Gbit/s THz kilometer-level wireless transmission link, which can significantly enhance the receiver performance and reduce the computational complexity. For the proof of concept, we create the 10 Gbaud sub-THz prototype to validate the proposed scheme over 4.6km wireless long-distance. The results demonstrate that the proposed scheme achieves a 0.7dB improvement in receiver sensitivity and a one-hundred-times reduction in real-valued multiplications per symbol (RMps) compared to the classical ones.

We have experimentally demonstrated terahertz (THz) wireless transmission of 27.9 Gbit/s orthogonal frequency division multiplexing (OFDM) signal at 335 GHz over 10 km fiber and 400 m wireless link, employing advanced digital signal processing (DSP) algorithms. As we know, based on the photonics-aided scheme, THz-wireless OFDM signal transmission is achieved for the first time over record-breaking 400 m wireless distance.