论文标题

非马克维亚开放QUTRIT系统中绝热加速的随机学习控制

Stochastic learning control of adiabatic speedup in a non-Markovian open qutrit system

论文作者

Xie, Yang-Yang, Ren, Feng-Hua, He, Run-Hong, Ablimit, Arapat, Wang, Zhao-Ming

论文摘要

量子系统的精确控制对于执行量子信息处理任务至关重要。就泄漏消除操作员方法而言,对于封闭的系统,理想的脉冲控制条件是通过P-Q分区技术得出的。但是,为开放系统设计相应的控制脉冲是一个挑战,这需要解决嘈杂的环境。在本文中,我们将随机搜索程序应用于开放的QUTRIT系统,并成功获得了最佳的绝热加速脉冲。计算结果表明,这些最佳脉冲使我们获得比理想脉冲更高的保真度。忠诚度的改善对于相对强大的系统浴耦合强度和高浴温度很大。对于某些耦合强度和浴室温度,对于代表环境的记忆时间的临界特征频率,可以实现最大改进。我们的调查表明,随机搜索程序是设计控制脉冲的强大工具,以打击环境的有害影响。

Precise and efficient control of quantum systems is essential to perform quantum information processing tasks. In terms of adiabatic speedup via leakage elimination operator approach, for a closed system, the ideal pulse control conditions have been theoretically derived by P-Q partitioning technique. However, it is a challenge to design the corresponding control pulses for an open system, which requires to tackle noisy environments. In this paper, we apply the stochastic search procedures to an open qutrit system and successfully obtain the optimal control pulses for significant adiabatic speedup. The calculation results show that these optimal pulses allow us to acquire higher fidelities than the ideal pulses. The improvement of fidelity is large for relatively strong system-bath coupling strength and high bath temperature. For certain coupling strength and bath temperature, the maximal improvement can be achieved for a critical characteristic frequency which represents the memory time of the environment. Our investigation indicates that the stochastic search procedures are powerful tools to design control pulses for combating the detrimental effects of the environment.

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