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.

翻译：量子计算展现出巨大潜力，但误差问题构成重大挑战。本研究探索了利用人工神经网络（ANN）与杨-巴克斯特方程（YBE）实现量子误差缓解的新策略。区别于传统计算密集型的误差校正方法，我们研究了人工误差缓解方案。本文介绍了量子误差来源的基本原理，并探讨了利用经典计算进行误差缓解的潜在可能。杨-巴克斯特方程在此过程中发挥关键作用，使我们能够将时间动力学模拟压缩至恒定深度量子线路。通过YBE引入可控噪声，我们增强了误差缓解数据集的质量。基于量子模拟的部分数据训练ANN模型，验证了其在修正时间演化量子态误差方面的有效性。