长短期记忆预测的开量子系统动力学最优控制
Optimal control of open-quantum-system dynamics predicted by long short-term memory
AI总结：
提出基于LSTM预测的开量子系统动力学的最优控制框架，通过两能级系统绝热加速和自旋链量子态转移示例验证了保真度提升。
摘要：
高保真量子控制的实现对于量子信息处理至关重要，特别是在噪声环境中，控制策略必须同时实现精确操控和有效噪声抑制。传统的最优控制设计通常需要数值计算系统动力学。最近的研究表明，长短期记忆神经网络（LSTM）可以准确预测开放量子系统的时间演化。基于LSTM预测的动力学，我们提出了一个最优控制框架，用于开放量子系统中快速高效的最优控制设计。作为示例，我们将该框架应用于非马尔可夫环境下两能级系统的绝热加速和自旋链中的量子态转移的最优控制设计。对于绝热加速，我们的优化过程包括两步：驱动轨迹优化和零面积脉冲优化。两步的保真度均得到提升，证明了该方案的有效性。此外，该有效性在高维控制问题——自旋链量子态转移中得到了验证。我们的最优控制设计方案利用预测的动力学生成优化控制，在量子计算、通信和传感中具有广泛的应用潜力。
英文摘要：
The realization of high-fidelity quantum control is crucial for quantum information processing, particularly in noisy environments where control strategies must simultaneously achieve precise manipulation and effective noise suppression. Conventional optimal control designs typically require numerical calculations of the system dynamics. Recent studies have demonstrated that long short-term memory neural networks (LSTM) can accurately predict the time evolution of open quantum systems. Based on LSTM predicted dynamics, we propose an optimal control framework for rapid and efficient optimal control design in open quantum systems. As illustrative examples, we apply the proposed framework to design optimal control for adiabatic speedup in a two-level system and for quantum state transfer in a spin chain, both under non-Markovian environments. For adiabatic speedup, our optimization procedure involves two steps: driving trajectory optimization and zero-area pulse optimization. Fidelity improvements for both steps have been obtained, demonstrating the effectiveness of the scheme. Furthermore, this effectiveness is validated for quantum state transfer in a spin chain, which is a high dimensional control problem. Our optimal control design scheme utilizes predicted dynamics to generate optimized controls, offering broad application potential in quantum computing, communication, and sensing.
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