GPU-Enhanced PDE Solver Simulating Information Processing in Mach-Zehnder Interferometer

Numerical methods are essential for simulating transient physical phenomena, providing crucial support to engineers and researchers in fields such as defense, space, and the natural sciences. By modeling these phenomena with partial differential equations (PDEs), deeper insights can be gained. Among the various PDE-solving methods, the Crank-Nicolson technique stands out for its efficiency in handling time-dependent PDEs, offering precise and stable solutions across diverse applications. In this work, we present a 3$D$ GPU-accelerated code optimized for parallel execution in three-dimensional space-time domains. The PDE solver is applied to quantum wave function information processing, specifically in the context of the circular double-slit experiment. Additionally, we utilize the solver to simulate a dual Mach-Zehnder interferometer to model the Fenna-Matthews-Olson (FMO) complex, developing a nature-inspired algorithm based on its quantum dynamics. This simulation demonstrates near-perfect intelligence in a photosynthetic system without any neural network. This work may help in the design of solar cells, potentially increasing their efficiency to a greater extent.