Quantum Photonic Cognitive VLSI Platform for Agentic AI Autonomous Analytics and Deep Learning Acceleration in Next Generation Electric Vehicle and Aerospace Communication Networks

This work proposes a Quantum Photonic Cognitive VLSI architecture that integrates ultrafast photonic tensor processing with multi-agent AI control for autonomous analytics in electric-vehicle (EV) and aerospace networks. The hardware core employs silicon photonic Mach–Zehnder interferometer arrays to perform optical matrix multiplications at light speed, dramatically surpassing transistor-based parallelism. A higher-level Agentic AI layer orchestrates multiple neural reasoning agents via reinforcement learning, enabling self-adaptive task allocation and continuous federated learning across vehicle and spacecraft subsystems[1] [2]. A sub-terahertz optical–RF transceiver fabric replaces conventional buses to maintain real-time model synchronization in a hypothetical 7G-edge network, while an adaptive energy manager balances laser power and electronics to ensure thermal stability. We fabricated a 5 nm photonic-CMOS prototype chip and demonstrated it in two environments: a live EV powertrain control twin and a rocket avionics simulator. Using tunable lasers and high-speed scopes, we achieved sub-millisecond inference at 1550 nm, with an 8.4× throughput boost and 3.8× latency reduction relative to a purely electronic accelerator. Notably, the platform sustained operation across –60 °C to +130 °C using on-chip photonic temperature control. The results show >99% decision accuracy under federated operation, even with 20% induced bandwidth loss. These outcomes establish a new benchmark for self-learning quantum-photonic VLSI, paving the way for post-silicon AI in future mobility and space networks.