量子计算中态选择场电离的演化优化
Evolutionary Optimization of State Selective Field Ionization for Quantum Computing
摘要：
物理学中的状态选择性场电离检测技术需要通过复杂的原子状态空间进行特定的进展，以优化状态选择性和整体效率。对于大的主量子数n，理论模型在计算上变得难以处理，并且任何结果通常由于与理想实验条件（例如外部电磁场）的小偏差而变得无关。几种不同的量子信息处理方案严重依赖于这些探测器的质量。在本文中，我们证明了一个原则，它是可能的，通过运行遗传算法来控制实验本身的方面，以优化实验现场配置文件。一个简单的实验产生了新的结果，与现有结果的分析一致。
英文摘要：
State selective field ionization detection techniques in physics require a specific progression through a complicated atomic state space to optimize state selectivity and overall efficiency. For large principle quantum number n, the theoretical models become computationally intractable and any results are often rendered irrelevant by small deviations from ideal experimental conditions, for example external electromagnetic fields. Several different proposals for quantum information processing rely heavily upon the quality of these detectors. In this paper, we show a proof of principle that it is possible to optimize experimental field profiles in situ by running a genetic algorithm to control aspects of the experiment itself. A simple experiment produced novel results that are consistent with analyses of existing results.
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