High-Speed Single-Physical-Hardware Deep Photonic Reservoir Computing Based on a Spin-VCSEL

As a bio-inspired paradigm fully implementable in optics, deep reservoir computing (RC) has emerged as a powerful computational framework for efficient information processing. However, conventional deep RC architectures typically demand substantial hardware overhead to establish deep nonlinear mapping. To address this, we propose a high-speed, single-physical-hardware deep photonic RC architecture based on an optically pumped spin vertical-cavity surface-emitting laser (spin-VCSEL). At the architectural level, an all-optical deep RC structure is established within a single-physical-hardware platform by feedforward-injecting the right-circularly polarized response into the left-circularly polarized mode, fully exploiting the nonlinear dynamics without another reservoir laser. Algorithmically, a compression–decompression framework is integrated to mitigate the latency overhead induced by time-division multiplexing without compromising performance. Such state reconstruction enables a tenfold reduction in the required optical-domain time-division multiplexing (TDM) processing duration. Numerical simulations demonstrate that the proposed system exhibits superior precision compared to conventional setups, yielding a normalized mean square error of 0.0039 in Santa Fe time-series prediction, a symbol error rate of 0.003 in nonlinear channel equalization, and a linear memory capacity of 19.82. Cross-correlation analysis supports reliable information transfer between the two polarization modes. Ultimately, this hardware-software co-designed paradigm offers a compact, low-latency platform for optical neuromorphic processing.