In the context of optical computing, photonic reservoir computing emerges as a scalable, energy-saving, and noise-robust alternative to quantum computing for machine learning. However, existing methods often lack the flexibility to finely control nonlinearities in the optical reservoir for improved performance. Here, we propose a novel photonic reservoir computing system based on spatial nonlinear wave propagation in erbium-doped multimode fibres (ED-MMF). Utilising phase-only spatial light modulators, we structure pump and probe beams in the fibre to encode and process information. Through nonlinear interactions between signal and pump modes within the gain medium, the ED-MMF enables a tunable nonlinear transformation of the input field, allowing control over nonlinear coupling between different fibre modes via accessible parameters like pump and signal power. By dynamically tuning the degree of nonlinearity, our system can identify optimal operating conditions for the reservoir, promising enhanced optical computing capabilities with potential applications in advanced machine learning tasks.
Terahertz (THz) is an innovative form of electromagnetic radiation providing unique spectroscopy capabilities in critical fields, ranging from biology to material science and security. The limited availability of high-resolution imaging devices, however, constitutes a major limitation in this field. In this work, we tackle this challenge by proposing an innovative type of time-space nonlinear Ghost-Imaging (GI) methodology that conceptually outperforms established single-pixel imaging protocols. Our methodology combines nonlinear pattern generation with time-resolved single-pixel measurements, as enabled by the state-of-the-art Time-Domain Spectroscopy (TDS) technique. This approach is potentially applicable to any wave-domain in which the field is a measurable quantity. The full knowledge of the temporal evolution of the transmitted field enables devising a new form of full-wave reconstruction process. This gives access not only to the morphological features of the sample with deeply subwavelength resolution but also to its local spectrum (hyperspectral imaging). As a target application, we consider hyperspectral THz imaging of a disordered inhomogeneous sample.
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