Quantum Error

Quantum error, stemming from imperfections in quantum hardware, severely limits the accuracy and scalability of quantum computations. Current research focuses on mitigating these errors through various strategies, including machine learning-based approaches employing neural networks (e.g., transformers, convolutional neural networks, and recurrent networks) and graph-based methods to optimize quantum circuit layouts and improve decoding of quantum error correction codes. These advancements are crucial for realizing fault-tolerant quantum computers and enabling practical applications of quantum algorithms in fields like optimization and machine learning.