论文标题
通过机器学习技术来控制增强的强场电离中的量子效应
Controlling quantum effects in enhanced strong-field ionisation with machine-learning techniques
论文作者
论文摘要
我们使用机器学习技术研究了强激光场中硅藻分子的电离电离中的非古典途径和量子干扰。量子干扰提供了一个桥梁,可促进分子内种群转移。它的频率高于该领域的频率,该频率是系统的固有性,并取决于几个因素,例如初始波袋的状态或核间分离的状态。使用降低性降低技术,即T-分布的随机邻居嵌入(T-SNE)和主成分分析(PCA),我们一次研究了多个参数的影响,并找到了在静态场中增强电离的最佳条件,并在静态场中进行了电离,并控制了对受控的电离释放的电离。这种控制的电离在时间依赖性的自相关函数中表现为阶梯式行为。我们解释了带有相位参数遇到的特征,还建立了通过相位空间Wigner Qasiprobity流控制电离的参数层次结构,例如状态的特定相干叠加,电子定位和核次内部距离范围。
We study non-classical pathways and quantum interference in enhanced ionisation of diatomic molecules in strong laser fields using machine learning techniques. Quantum interference provides a bridge, which facilitates intramolecular population transfer. Its frequency is higher than that of the field, intrinsic to the system and depends on several factors, for instance the state of the initial wavepacket or the internuclear separation. Using dimensionality reduction techniques, namely t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), we investigate the effect of multiple parameters at once and find optimal conditions for enhanced ionisation in static fields, and controlled ionisation release for two-colour driving fields. This controlled ionisation manifests itself as a step-like behaviour in the time-dependent autocorrelation function. We explain the features encountered with phase-space arguments, and also establish a hierarchy of parameters for controlling ionisation via phase-space Wigner quasiprobability flows, such as specific coherent superpositions of states, electron localisation and internuclear-distance ranges.