  Photonic reservoir computing (PRC) is a special hardware recurrent neural
network, which is featured with fast training speed and low training cost. This
work shows a wavelength-multiplexing PRC architecture, taking advantage of the
numerous longitudinal modes in a Fabry-Perot semiconductor laser. These modes
construct connected physical neurons in parallel, while an optical feedback
loop provides interactive virtual neurons in series. We experimentally
demonstrate a four-channel wavelength-multiplexing PRC, which runs four times
faster than the single-channel case. It is proved that the multiplexing PRC
exhibits superior performance on the task of signal equalization in an optical
fiber communication link. Particularly, this scheme is highly scalable owing to
the rich mode resources in Fabry-Perot lasers.
