Novel Quantum Optical Physical Unclonable Function with AI-Driven Stabilization for FPGA-based Authentication Systems

This paper introduces a novel, AI-stabilized Quantum Optical Physical Unclonable Function (QO-PUF) designed for secure hardware authentication on embedded platforms.The system exploits the complexity of coherent light scattering through a disordered TiO2doped polymer medium to produce high-entropy, physically unclonable response patterns.To mitigate environmental sensitivitysuch as thermal fluctuations, mechanical shifts, and optical misalignment-an 8-bit quantized convolutional neural network (CNN) is implemented entirely on a Xilinx Artix-7 FPGA.This CNN-based stabilization corrects distortions in real-time, reducing the bit error rate (BER) from 18% to below 2.1% under stress conditions.The architecture achieves rapid challenge-response evaluation in under 5 milliseconds, while consuming only 30% LUT, 18% BRAM, and 12% DSP, making it ideal for latency-constrained embedded security applications.Extensive testing across 50,000+ challenge-response pairs confirms high uniqueness, reproducibility, entropy