Quantum Hamiltonian identification is important for characterizing thedynamics of quantum systems, calibrating quantum devices and achieving precisequantum control. In this paper, an effective two-step optimization (TSO)quantum Hamiltonian identification algorithm is developed within the frameworkof quantum process tomography. In the identification method, different probestates are inputted into quantum systems and the output states are estimatedusing the quantum state tomography protocol via linear regression estimation.The time-independent system Hamiltonian is reconstructed based on theexperimental data for the output states. The Hamiltonian identification methodhas computational complexity O(d^6) where d is the dimension of the systemHamiltonian. An error upper bound O(d^3/N^(1/2))$ is also established, where Nis the resource number for the tomography of each output state, and severalnumerical examples demonstrate the effectiveness of the proposed TSOHamiltonian identification method.
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机译：量子哈密顿量识别对于表征量子系统的动力学，校准量子器件并实现精确的量子控制非常重要。本文在量子过程层析成像的框架内，开发了一种有效的两步优化（TSO）量子哈密顿量识别算法。在识别方法中，将不同的探测状态输入到量子系统中，并使用量子状态层析成像协议通过线性回归估计来估计输出状态。基于输出状态的实验数据，构造与时间无关的系统哈密顿量。哈密顿识别方法具有计算复杂度O（d ^ 6），其中d是系统哈密顿量的维数。还建立了误差上限O（d ^ 3 / N ^（1/2））$，其中Nis是每个输出状态的层析成像的资源编号，并且几个数字示例证明了所提出的TSOHamiltonian识别方法的有效性。
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