Nontechnical Description: The increasing data volumes from artificial intelligence (AI), internet of things, and 5G/6G networks is challenging the processing power of CMOS-based computing hardware. To extend the computing power scaling and energy efficiency, we propose light-based photonic integrated computing circuits for computing at high clockrates and with ultralow-loss on-chip data movement. This project supports the national interest by significantly reducing the energy required for AI computations—potentially two orders of magnitude more efficient than current CMOS technologies—paving the way for powerful and sustainable computing systems. Its outcomes could revolutionize applications from autonomous vehicles and healthcare diagnostics to natural language processing and scientific discovery. The project will also strengthen U.S. competitiveness in semiconductor manufacturing by training a new generation of experts in chip design, photonics, and AI. Importantly, it will promote STEM participation through hands-on training and outreach. Technical Description: This project aims to build high efficiency (>100 TOPS/W), high-throughput photonic-electronic hybrid processors by leveraging wafer-scale heterogeneous integration of thin-film lithium niobate (TFLN) and silicon photonics/electronics. The key technical goals include: (1) developing space-time-wavelength hyperdimensional photonic circuits that can perform massive parallel tensor computations using scalable time-