In recent years, as an important driving force for the new round of technological revolution and industrial transformation, artificial intelligence technology has shone brightly in the fields of big data, cloud computing, the Internet of Things, data centers, and radio management. At the same time, the rapid development of artificial intelligence and information technology has also led to an explosive growth in the scale of information that needs to be processed globally. However, due to the bottleneck of high-end semiconductor manufacturing processes in China, traditional “von Neumann” architecture-based electronic processors struggle to support the requirements for computing power and energy consumption. Therefore, the search for a new type of computing with fast information processing speed and low power consumption has become a major challenge for artificial intelligence technology. Neuromorphic computing is a type of computing method that simulates the information-processing process of the human brain. Since its emergence, it has attracted great attention. Among these, reservoir computing (RC), as a simple and efficient neuromorphic computing framework similar to the cortical circuits of the human brain, has received much attention. The core idea is to use the dynamical system as a reservoir layer (nonlinear generalization of the standard basis) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Traditional recurrent neural networks face problems such as low computing efficiency, complex training algorithms, and easy entrapment in local optima, while reservoir computing has the advantages of fast learning speed and low training cost. In addition, the rapid development of photonics technology and optical devices has also brought new opportunities to optical information processing. Photonic reservoir systems, with their remarkable advantages of high speed, low latency, wide bandwidth, and multi-dimensionality, have quickly become a research hotspot.

The theoretical and experimental research on photonic RC has rapidly progressed along two main directions. The first direction involves the construction of spatially distributed array photonic RC systems with multiple physical nodes. In this approach, various photonic devices are carefully selected and arranged to form specific spatial array structures, creating a photonic reservoir. By leveraging the propagation and interaction of photons among these nodes, the system can perform complex processing of input optical signals to accomplish corresponding computational tasks. This direction can be further divided into two sub-approaches: one uses multiple optical devices as nonlinear nodes—such as semiconductor optical amplifiers (SOAs) and microring resonators—and constructs optical networks through waveguide coupling (Fig. 2). The other focuses on free-space optical modulation, utilizing components like spatial light modulators (SLMs) and diffractive optical elements (DOEs). These systems achieve spatial distribution through methods such as secondary imaging and phase modulation, forming spatially distributed array photonic RC systems (Fig. 3).The second direction centers on building nonlinear photonic reservoirs using different optical devices and delayed feedback loops. Based on the principle of time-division multiplexing, equally spaced sampling points along the feedback loop are used to replace real physical nodes in space, resulting in hardware-friendly time-delay photonic RC systems. This paper focuses on time-delay RC systems based on semiconductor lasers (Fig. 7 and Fig. 10). Finally, this paper also discusses the challenges faced in this field and the emerging research directions, including the deployment of photonic RC in practical applications, the lack of universal reservoir operators, the implementation of all-optical reservoirs, and the gap between existing results and solving real-world problems.

Photonic RC hardware demonstrates vast application potential in cutting-edge fields such as 6G communications, next-generation optical networks, the internet of things (IoT), green data centers, intelligent robotics, and digital twins, and is expected to become a core driving force for technological innovation and industrial upgrading. However, despite its potential for large-scale deployment, photonic RC still faces numerous challenges in practical implementation. On the theoretical front, current universal approximation theories mainly focus on existence proofs, lacking the design of general-purpose operators based on reservoir architectures and the realization of reconfigurable universal computation grounded in such reservoirs. In data-driven control applications, the output layer of existing reservoir computing frameworks still relies heavily on digital software-based implementations. How to design a fully hardware-based output layer, ultimately achieving all-optical reservoir computing, remains a major challenge for researchers. From an algorithmic perspective, although reservoir computing holds significant promise, a gap still exists between current research outcomes and the ability to solve complex, real-world problems. Coordinated breakthroughs in structural design, theoretical analysis, algorithm optimization, and hardware integration are urgently needed to bring reservoir computing into practical, real-world applications.