量子集合分类：基于采样的学习控制方法
Quantum Ensemble Classification: A Sampling-based Learning Control Approach
AI总结：
本文提出基于采样的学习控制方法，用于解决非正交态的量子集合分类问题，通过同时引导不同类成员到目标态实现有效分类。
摘要：
量子集合分类在原子（或分子）鉴别、同位素分子分离和量子信息提取中有重要应用。然而，量子力学禁止非正交态的确定性鉴别。由于不同类别成员的参数存在变化，非均匀量子集合的分类极具挑战性。本文将量子集合分类重新表述为监督量子学习问题。通过采用基于采样的学习控制（SLC）方法，提出系统化的分类方法。通过同时引导不同类成员到对应的目标态（如正交态）完成分类任务。首先提出一种新的两种相似量子系统的鉴别方法。然后提出用于量子集合分类的SLC方法。数值结果表明，所提出的方法在二元量子集合二分类和多类多级量子集合多类分类中均有效。
英文摘要：
Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast quantum ensemble classification as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). Firstly a new discrimination method is proposed for two similar quantum systems. Then an SLC method is presented for quantum ensemble classification. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.
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