With the advent of the information era, the development of artificial intelligence technology has undergone an unprecedented transformation, leading to a growing demand for computing resources and efficiency. However, conventional computers based on electronic transistors, constrained by von Neumann architectures, encounter performance bottlenecks in complex computing tasks such as deep neural networks and large-scale combinatorial optimizations. To circumvent the limitations of traditional computing hardware systems, researchers have begun exploiting the inherent properties of different physical systems for computation or computing acceleration, including quantum computing, DNA computing, neuromorphic computing, and optical computing.
Among the innovative computing approaches mentioned above, optical computing aims to construct all-optical or optoelectronic systems for information processing by leveraging the physical properties of light and the intricate interactions between light and matter. Optical computing excels in many complex computing tasks due to ultra-fast transmission speeds, high parallelism, and minimal energy consumption. Since the proposal of the optical correlator in the 1950s and 1960s, optical computing has consistently drawn the attention of researchers across various fields. With emerging concepts, such as on-chip optical neural networks, diffractive deep neural networks, optoelectronic reservoir computing, and photonic Ising machines, the application potential of photons in diverse complex computing tasks is becoming increasingly evident. It is not an exaggeration to state that photons are evolving into one of the foundations of next-generation computing.
Lasers, as high-performance light sources, play a crucial role in industrial manufacturing and scientific research. Generated by laser cavities, laser has been widely employed in fabrication, measurement, communication, medicine, and other fields. In recent years, researchers have discovered that lasers can also serve as powerful computational tools. Specifically, the randomness and nonlinearity of lasers in chaotic oscillations, relaxation oscillations, and other unsteady states can be harnessed to address complex calculation problems. Additionally, in the absence of external disturbances, physical processes, such as mode competition, can cause the light field in laser cavities to spontaneously evolve into a stable oscillation state with the lowest loss, which can be mapped to the solution of a complex computation problem. As optical computing continues to advance, and laser generation, control, and detection technologies mature, there is a growing interest in the computational capabilities of lasers. Therefore, it is essential to summarize the progress of optical computing based on laser cavities to guide the further integration of lasers and artificial intelligence technology, ultimately promoting the development of intelligent laser computing systems.
In this review, we comprehensively summarize the recent progress in optical computing based on laser cavities, primarily focusing on reinforcement learning using laser chaos, reservoir computing by lasers with optical delayed feedback, and spin models for solving combinatorial optimization problems simulated by laser networks.
Firstly, we introduce methods that utilize laser chaos signals generated by semiconductor lasers to perform reinforcement learning (RL). Naruse et al. initially demonstrated RL assisted by laser chaos, which served as random numbers, and proved that laser chaos signals outperform pseudorandom numbers generated by conventional electronic circuitry in this calculation task. Subsequent research aiming at scalability and parallelism improvement is also discussed (Fig. 2). To further exploit the properties of oscillations within laser cavities, RL based on mode switching in a ring laser, lag synchronization of coupled lasers and laser networks, and chaotic itinerancy in a multimode semiconductor laser have been proposed and demonstrated as well (Fig. 3).
Subsequently, we discuss optoelectronic reservoir computing (RC) based on lasers, mainly focusing on delay-based RC using lasers with optical delayed feedback. Since 2013, when Brunner et al. experimentally implemented reservoir computing using a semiconductor laser as the nonlinear node, numerous studies have been conducted to enhance performance. These include RC based on semiconductor ring lasers, microchip lasers, vertical cavity surface-emitting lasers, and photonic integrated circuits (Fig. 6, Fig. 7).
Finally, we review recent advances in simulating spin models using laser networks. Artificial spin models can be employed to solve NP-hard combinatorial optimization problems, as their ground states are associated with the solutions. Under certain circumstances, the steady oscillation state of a laser network system can be mapped to the ground state of the spin Hamiltonian, and thus, to the solution of the combinatorial optimization problem. Photonic Ising machines based on injection-locked laser networks (Fig. 8) and XY model simulators based on degenerate cavity lasers (Fig. 11) are outlined, respectively. Additionally, other types of challenging computational problems solved by degenerate cavity lasers are presented, including real-time wavefront shaping, phase retrieval, generation of arbitrary-shaped laser beams, and real-time full-field imaging through scattering media (Fig. 12).
In addition to the inherent advantages of optical computing, such as ultra-fast transmission speed, high parallelism, and negligible energy consumption, laser-based optical computing fully utilizes the unique physical processes occurring in laser cavities, as well as various mature laser technologies, to provide a wealth of solutions for complex computing tasks. In the future, the theoretical model of optical computing based on laser cavities needs further optimization to continuously expand its application in various intelligent computing fields and to improve calculation accuracy, scale, and dimension. Additionally, with the exploration and development of intelligent algorithms and optoelectronic devices that are better suited for optical computing, combined with rapidly advancing online training and in situ training schemes, intelligent laser computing is expected to gradually achieve all-optical, high-efficiency, and real-time performance. Furthermore, by utilizing novel laser cavity structures, advanced laser technologies, and photon integration technologies, along with metamaterial and metasurface technologies, it is anticipated that more compact on-chip intelligent laser computing systems will be developed. In summary, the establishment of high-speed and high-efficiency intelligent laser systems for information processing and computation is a significant and promising research direction that encompasses the simultaneous development of hardware, software, and algorithms.
