The goal of this paper is the presentation of the elementary procedures that normally are done in nonrelativistic Quantum Mechanics in terms of the principles of Machine Learning. In essence, this paper discusses Mitchell’s criteria, whose block fundamental dictates that the universal evolution of any system is composed by three fundamental steps: (i) Task, (ii) Performance and (iii) Experience. In this paper, the quantum mechanics formalism reflected on the usage of evolution operator and Green’s function are assumed to be part of mechanisms that are inherently engaged to the Machine Learning philosophy. The action for measuring observables through experiments and the intrinsic apparition of statistical or systematic errors are discussed in terms of “quantum learning”.

中文翻译：

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基于非相对论量子力学的机器学习理论

本文的目标是根据机器学习的原理介绍通常在非相对论量子力学中完成的基本程序。从本质上讲，本文讨论了 Mitchell 的标准，其基本原理表明任何系统的普遍进化都由三个基本步骤组成：（i）任务，（ii）性能和（iii）经验。在本文中，反映在进化算子和格林函数使用上的量子力学形式主义被假定为与机器学习哲学固有的机制的一部分。在“量子学习”方面讨论了通过实验测量可观察量的行为以及统计或系统误差的内在显现。