基于量子系综分类的量子系统哈密顿量辨识
Hamiltonian identification for quantum systems via quantum ensemble classification

针对量子系统中的哈密顿量辨识问题, 提出了一种基于量子系综分类的量子系统参数辨识方法. 首先, 所 采用的量子系综分类方法结合了基于采样的学习控制方法和梯度流算法, 可利用所设计的控制场有效区分具有不 同哈密顿量参数的量子系统; 其次, 以交叉验证的方式对于所需估计的哈密顿量参数值进行区间判定, 提高估计可 靠性; 再次, 采用逐次细化判定区间的方法, 辨识出最终的哈密顿量参数; 最后, 通过数值仿真验证了所提出的量子 系统哈密顿量辨识方法的有效性和实用性.
In this paper, a hamiltonian identification approach is proposed for quantum systems using quantum ensemble classification. First, the quantum ensemble classification method is introduced by combining sampling-based learning control and gradient flow algorithms, which helps discriminate different quantum systems whose hamiltonian parameters falling in different intervals, respectively. Second, the intervals for the parameters to be identified are estimated via cross verification to achieve a reliable result. Third, the hamiltonian parameters are identified by successively refining the estimated intervals. Finally, the effectiveness and practicability of the proposed hamiltonian identification approach is verified using numerical simulation.