量子惯性测量技术是一种将微观物质的量子效应作为测量载体的精密测量技术，可用于精确测量加速度、角速度等惯性量。在具体实现中，其测量精度、灵敏度等性能指标受到多种内在和外界条件的影响和制约。例如，环境气温、气压、湿度、冷原子温度、光强和相位分布、机械振动等都会对测量性能产生影响。这些因素产生的影响具有高度的复杂性，并且彼此之间存在复杂耦合，难以精确建立物理和数学模型，因此传统的误差分析方法不能有效地处理这些影响。.机器学习算法是一种使用过往数据进行“训练”，从而“预测”新数据的计算机算法。相比于传统的分析方法，机器学习算法无需针对具体实验系统和影响因素建立模型，而是通过建立各种影响因素之间的耦合和因果关联，对经过修正的实验测量真值进行预测，特别适用于复杂的量子惯性测量系统。本研究将机器学习算法用于分析和修正量子惯性测量系统的测量结果，降低误差和噪声影响，提高量子惯性测量系统的测量精度和灵敏度。

Quantum inertial sensing is a type of precision measurement technology that utilizes the quantum effects of microscopic materials as the measurement carrier, and can be used to precisely measure the inertia such as acceleration and angular velocity. In its realization, the measurement accuracy, sensitivity and other metrological specs are subject to a variety of conditions and constraints both internal and external. For example, ambient temperature, air pressure, humidity, cold atom temperature, laser intensity and phase distribution, mechanical vibration, etc. have an impact on the measurement. The impact of these factors is highly complex, and coupled with each other, making it difficult to accurately establish physical and mathematical models, so conventional error analysis methods cannot effectively address these effects..Machine learning algorithms are computer algorithms that "train" from past data to "predict" the new data. Compared with the conventional analysis methods, the machine learning algorithm does not need modeling the specific experimental system and its influencing factors, but predicts the true value of the biased experimental measurement by establishing the coupling and causal relationship among various influencing factors. This technic is suitable especially for complex quantum inertial sensing systems. In this study, the machine learning algorithm is used to analyze and correct the measurement results of a quantum inertial measurement system, suppress the influence of error and noise, and improve the measurement accuracy and sensitivity of the quantum inertial measurement system.