论文标题
通过人工神经网络预测量子协议的最小控制时间
Predicting the minimum control time of quantum protocols with artificial neural networks
论文作者
论文摘要
量子控制取决于量子状态的驱动而不会丧失相干性，因此随着时间的推移，量子特性泄漏到环境上是一个基本挑战。一个工作是实施快速协议，因此最小的控制时间（MCT）至关重要。在这里，我们采用机器学习网络来估算状态转移协议中的MCT。通过将自动编码器网络与K-Means聚类工具的组合结合来考虑一种无监督的学习方法。鉴于当总进化时间在MCT之下或超过MCT之下时，对控制景观具有分析性MCT和独特的拓扑变化，对Hamiltonian进行了分析。我们可以获得网络不仅能够对MCT进行估计，还可以了解景观的拓扑结构。对于广义的LZ Hamiltonian，发现了类似的结果，而遇到了我们非常简单的架构的局限性。
Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties onto the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k-means clustering tool. The Landau-Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape's topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.
