Supervised-learning-assisted coupling design for enhanced transmission fidelity in a spin chain under time-varying noise

High-fidelity quantum state transfer (QST) is one of the many facets required in generic quantum computing devices. For this short distance communication, perfect state transfer (PST) through spin chains can be realized by PST couplings design. However, the transmission fidelity is often degraded by the environmental noise, which can be quasi-static or time-varying. While conventional optimization techniques have been developed to address quasi-static noise, mitigating time-varying noise remains a challenge. Building upon the PST coupling framework, we employ a neural network (NN)-based supervised learning (SL) approach to optimize the coupling design for enhanced transmission fidelity in a spin chain against time-varying noise. By incorporating environmental parameters into the dataset, the trained NN identifies coupling configurations that are robust to both quasi-static and time-varying noise. Our method introduces an optimal design strategy to address time-varying noise, with applications across diverse quantum information processing tasks.