Learning models of quantum systems from experiments
Learning models of quantum systems from experiments
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DOI：
10.1038/s41567-021-01201-7
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发表时间：
2021-04-29
期刊：
影响因子：
19.6
通讯作者：
Laing, Anthony
中科院分区：
文献类型：
--
作者：
Gentile, Antonio A.;Flynn, Brian;Laing, Anthony
由于哈密顿模型是物理和化学过程研究和分析的基础，因此它们忠实于它们所代表的系统至关重要。然而，从实验数据中制定和测试量子系统的候选哈密顿量是很困难的，因为人们无法直接观察存在哪些相互作用。在这里，我们提出并演示了一种自动化协议，通过设计一个利用无监督机器学习的代理来克服这一挑战。我们首先展示了我们的方法在研究氮空位中心设置时推断正确哈密顿量的能力。在初步模拟中，准确的模型是已知的，并且可以正确推断，成功率高达 59%。当使用实验数据时，74% 的协议实例检索到被认为合理的模型。模拟多自旋系统的特征是 10(10) 个可能模型的空间，我们还通过在我们的协议中纳入遗传算法来研究该系统，该算法在 85% 的实例中识别目标模型。自动化代理的开发能够根据有限的先验假设来制定和测试建模假设，这代表了表征大型量子系统的基本一步。量子系统使得从实验数据中确定候选哈密顿量变得具有挑战性。提出了一种自动化协议，并在氮空位中心设置中证明了其推断正确哈密顿量的能力。
As Hamiltonian models underpin the study and analysis of physical and chemical processes, it is crucial that they are faithful to the system they represent. However, formulating and testing candidate Hamiltonians for quantum systems from experimental data is difficult, because one cannot directly observe which interactions are present. Here we propose and demonstrate an automated protocol to overcome this challenge by designing an agent that exploits unsupervised machine learning. We first show the capabilities of our approach to infer the correct Hamiltonian when studying a nitrogen-vacancy centre set-up. In preliminary simulations, the exact model is known and is correctly inferred with success rates up to 59%. When using experimental data, 74% of protocol instances retrieve models that are deemed plausible. Simulated multi-spin systems, characterized by a space of 10(10) possible models, are also investigated by incorporating a genetic algorithm in our protocol, which identifies the target model in 85% of instances. The development of automated agents, capable of formulating and testing modelling hypotheses from limited prior assumptions, represents a fundamental step towards the characterization of large quantum systems.Quantum systems make it challenging to determine candidate Hamiltonians from experimental data. An automated protocol is presented and its capabilities to infer the correct Hamiltonian are demonstrated in a nitrogen-vacancy centre set-up.