Core Viewpoint - The demand for computing power is increasing across various industries, leading to the emergence of optical computing technology as a promising alternative to traditional electronic computing architectures, which are limited by the "von Neumann bottleneck" and the early-stage development of quantum computing [1] Group 1: Advantages of Optical Computing - Optical computing utilizes light as a medium, offering significant advantages such as high speed, low energy consumption, and the ability to perform parallel computations due to multiple physical dimensions of light [2] - The energy efficiency of optical devices is notable, as they generate minimal heat during operation, making them suitable for high-density tasks like scientific computing and machine learning [2] - Optical devices exhibit superior bandwidth and speed, allowing for rapid processing of broadband analog signals with almost no latency [2] Group 2: Different Architectures in Optical Computing - Free Space Optics (FSO) is one of the earliest forms of optical computing, utilizing lenses and spatial light modulators to manipulate light in air or vacuum, but faces challenges in durability and reliability [3] - Photonic chips integrate miniature optical components and can be easily incorporated into existing electronic architectures, although many solutions struggle with scalability for complex tasks [3] - Fiber optic systems leverage established fiber communication infrastructure for complex calculations, particularly in optimization problems and AI, but often rely on electronic devices for key functions, which can slow down processing [4] Group 3: Technical Bottlenecks and Future Prospects - The current phase of optical computing is critical, with a pressing global need for faster, more environmentally friendly computing solutions, presenting opportunities for optical systems to complement or surpass traditional silicon-based systems [5] - Short-term prospects favor all-optical free space systems and hybrid systems that combine optical and electronic components, while "memory computing" architectures show significant potential [5] - Mid-term developments may focus on new processing architectures that integrate spatial and temporal dimensions for enhanced performance and efficiency [6] - Key technical challenges include precision and stability, optical data storage, and integration and packaging, with ongoing research aimed at overcoming these hurdles through innovations like 3D packaging and new materials [8]