The classical limit of quantum mechanics, formally investigated through frameworks like strict deformation quantization, remains a profound area of inquiry in the philosophy of physics. This paper explores a computational approach employing a neural network to emulate the emergence of classical behavior from the quantum harmonic oscillator as Planck's constant $\hbar$ approaches zero. We develop and train a neural network architecture to learn the mapping from initial expectation values and $\hbar$ to the time evolution of the expectation value of position. By analyzing the network's predictions across different regimes of hbar, we aim to provide computational insights into the nature of the quantum-classical transition. This work demonstrates the potential of machine learning as a complementary tool for exploring foundational questions in quantum mechanics and its classical limit.

本研究解决了量子力学经典极限的计算问题，探讨了神经网络如何模拟量子谐振子的经典行为。在训练的网络中，我们成功实现了从初始期望值和普朗克常数到位置期望值时间演化的映射，揭示了量子与经典之间转变的本质。这项工作展示了机器学习在研究量子力学基础问题中的潜力。

通过学习可观察量映射的神经网络模拟量子系统中的经典极限