This study presents an all-optical approach based on an XOR logic gate for encryption by interference and the assistance of multiphotonic effects exhibited by carbon nanotubes. We integrate a Michelson interferometer to propose the encryption system. The key innovation lies in the use of multiwalled carbon nanotubes (MWCNT) to control the XOR operation through intensity-dependent nonlinear optical absorption. We introduce control based on nanosecond nonlinear optical absorption in MWCNT. By measuring irradiance propagation through thin-film samples of MWCNT, we demonstrate a threshold-based binary data recording system that is highly resistant to unauthorized access. The combination of interferometric response, MWCNT-based intensity control, and multicriteria decision analysis through nonlinear absorption presents a powerful and versatile approach to optical encryption. This method has the potential to be a base for secure communication systems and optical computing, with possible extensions to biological computing and microbiology. While challenges in power optimization and scaling remain, this research marks a significant step towards advanced, ultrafast encryption systems.

This study explores the integration of reduced graphene oxide (rGO) into an optoelectronic XOR logic gate to enhance BB84 protocol encryption in quantum communication systems. The research leverages the nonlinear optical properties of rGO, specifically its nonlinear refraction characteristics, in combination with a Michelson interferometer to implement an optoelectronic XOR gate. rGO samples were deposited using the Langmuir–Blodgett technique and characterized in morphology and structure. The optical setup utilized a frequency-modulated laser signal for the interferometer and a pulsed laser system that generates the quantum information carrier. This integration of quantum encryption with nonlinear optical materials offers enhanced security against classical attacks while providing adaptability for various applications from secure communications to quantum AI.