  Quantum computing shows great potential, but errors pose a significant
challenge. This study explores new strategies for mitigating quantum errors
using artificial neural networks (ANN) and the Yang-Baxter equation (YBE).
Unlike traditional error correction methods, which are computationally
intensive, we investigate artificial error mitigation. The manuscript
introduces the basics of quantum error sources and explores the potential of
using classical computation for error mitigation. The Yang-Baxter equation
plays a crucial role, allowing us to compress time dynamics simulations into
constant-depth circuits. By introducing controlled noise through the YBE, we
enhance the dataset for error mitigation. We train an ANN model on partial data
from quantum simulations, demonstrating its effectiveness in correcting errors
in time-evolving quantum states.
