  Reservoir computing (RC) is a leading machine learning algorithm for
information processing due to its rich expressiveness. A new RC paradigm has
recently emerged, showcasing superior performance and delivering more
interpretable results with shorter training data sets and training times,
representing the next generation of RC computing. This work presents the first
realization of a high-speed next-generation RC system on an integrated photonic
chip. Our experimental results demonstrate state-of-the-art forecasting and
classification performances under various machine learning tasks and achieve
the fastest speeds of 60 Gbaud and a computing density of 103 tera
operations/second/mm$^2$ (TOPS/mm$^2$). The passive system, composed of a
simple star coupler with on-chip delay lines, offers several advantages over
traditional RC systems, including no speed limitations, compact footprint,
extremely high fabrication error tolerance, fewer metaparameters, and greater
interpretability. This work lays the foundation for ultrafast on-chip photonic
RC, representing significant progress toward developing next-generation
high-speed photonic computing and signal processing.
