Optimization methods for integrated and programmable photonics in next-generation classical and quantum smart communication and signal processing systems

The development of integrated and programmable photonic devices has significantly affected modern communications and signal processing in both the classical and quantum domains. However, achieving the required performance for new smart applications presents challenges in terms of design, fabrication, and control over multiple parameters. Optimization methods that leverage metaheuristic algorithms, machine learning, and artificial neural networks offer efficient solutions for the complex design of photonic devices, enabling new and desired functionalities. This comprehensive review explores the use of these methods to enhance the fabrication of innovative devices for smart photonic applications in next-generation communication and signal processing. We begin by introducing the mathematical frameworks of these optimization methods. We then investigate how they enable customization, optimization, and new device functionalities. Ultimately, we present our conclusions and discuss future prospects, emphasizing the potential of optimization methods in promoting revolutionary advancements in photonics.