Scalable and rapid programmable photonic integrated circuits empowered by Ising-model intelligent computation

Programmable photonic integrated circuits (PICs) have emerged as a promising platform for analog signal processing. Programmable PICs, as versatile photonic integrated platforms, can realize a wide range of functionalities through software control. However, a significant challenge lies in the efficient management of a large number of programmable units, which is essential for the realization of complex photonic applications. In this paper, we propose an innovative approach using Ising-model-based intelligent computing to enable dynamic reconfiguration of large-scale programmable PICs. In the theoretical framework, we model the Mach–Zehnder interferometer (MZI) fundamental units within programmable PICs as spin qubits with binary decision variables, forming the basis for the Ising model. The function of programmable PIC implementation can be reformulated as a path-planning problem, which is then addressed using the Ising model. The states of MZI units are accordingly determined as the Ising model evolves toward the lowest Ising energy. This method facilitates the simultaneous configuration of a vast number of MZI unit states, unlocking the full potential of programmable PICs for high-speed, large-scale analog signal processing. To demonstrate the efficacy of our approach, we present two distinct photonic systems: a 4×4 wavelength routing system for balanced transmission of four-channel NRZ/PAM-4 signals and an optical neural network that achieves a recognition accuracy of 96.2%. Additionally, our system demonstrates a reconfiguration speed of 30 ms and scalability to a 56×56 port network with 2000 MZI units. This work provides a groundbreaking theoretical framework and paves the way for scalable, high-speed analog signal processing in large-scale programmable PICs.