<i>Application of Machine Learning Techniques for Quantum</i><i>Mechanics Data Analysis: Predicting Particle Positions and Types</i>

Quantum mechanics is a fundamental field of physics that examines the behavior ofmicroscopic particles such as electrons and photons. With the rapid advancement of machinelearning (ML) technologies, there are significant opportunities to enhance quantum modelsand improve predictive accuracy. This study explores the application of machine learning topredict particle positions based on energy and velocity data, and to identify the type ofparticle (electron or photon) based on energy measurements.In this research, a linear regression model was developed to predict three-dimensionalparticle positions (x, y, z) from quantum mechanics data. The data was carefully prepared,including splitting into training and testing sets. The model's performance was evaluatedusing metrics such as Mean Squared Error (<b>MSE</b>) and R-squared (<b>R²</b>) to assess predictionaccuracy.Results indicate that the model provides high accuracy in predicting particle positions, withpredictions aligning closely with experimental data due to machine learning enhancements.Additionally, the model successfully identifies particle types based on energy with highaccuracy, presenting a robust tool for quantum data analysis.This study contributes significantly by integrating machine learning with quantummechanics, improving the precision of quantum models and expanding their application.Future research should explore advanced ML techniques on larger datasets and developmodels capable of handling various particles and quantum conditions, including potentialapplications in advanced quantum research and new measurement technologie