论文标题
通过学习算法优化绝热量子途径
Optimizing adiabatic quantum pathways via a learning algorithm
论文作者
论文摘要
设计适当的时间依赖性控制场，以缓慢改变系统到编码问题解决方案的基态，对于绝热量子计算至关重要。但是，实际应用中不可避免的扰动要求我们加速进化，以便可以防止绝热误差积累。在这里，通过将这项权衡任务视为多目标优化问题，我们提出了使用脉冲平滑技术的无梯度学习算法来搜索最佳的绝热量子途径，并将其应用于Landau-Zener Hamiltonian和Grover search hamiltonian。与线性时间表，局部绝热定理诱发的时间表以及基于梯度的算法搜索时间表的数值比较表明，所提出的方法可以在绝热时间和瞬时地面状态人口维护方面实现显着的性能改善。所提出的方法可用于解决更复杂和真正的绝热量子计算问题。
Designing proper time-dependent control fields for slowly varying the system to the ground state that encodes the problem solution is crucial for adiabatic quantum computation. However, inevitable perturbations in real applications demand us to accelerate the evolution so that the adiabatic errors can be prevented from accumulation. Here, by treating this trade-off task as a multiobjective optimization problem, we propose a gradient-free learning algorithm with pulse smoothing technique to search optimal adiabatic quantum pathways and apply it to the Landau-Zener Hamiltonian and Grover search Hamiltonian. Numerical comparisons with a linear schedule, local adiabatic theorem induced schedule, and gradient-based algorithm searched schedule reveal that the proposed method can achieve significant performance improvements in terms of the adiabatic time and the instantaneous ground-state population maintenance. The proposed method can be used to solve more complex and real adiabatic quantum computation problems.
