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Adaptive quantum state tomography with neural networks
npj Quantum Information ( IF 6.6 ) Pub Date : 2021-06-24 , DOI: 10.1038/s41534-021-00436-9
Yihui Quek , Stanislav Fort , Hui Khoon Ng

Current algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.



中文翻译:

具有神经网络的自适应量子态断层扫描

当前的量子态断层扫描 (QST) 算法在实验方面成本很高,需要测量许多状态副本,而在经典计算方面,需要很长时间来分析收集的数据。在这里,我们介绍了神经自适应量子态断层扫描 (NAQT),这是一种快速、灵活的基于机器学习的 QST 算法,可适应测量并提供数量级更快的处理,同时保持最先进的重建精度。与其他自适应 QST 方案一样,测量自适应利用从先前测量的状态副本收集的信息来执行对下一个副本的有针对性的感知,从而最大化从下一个副本收集的信息。我们的 NAQT 方法允许快速无缝地集成测量适应和统计推断,使用标准贝叶斯更新的神经网络替换,以获得状态的最佳估计。我们的算法属于“元学习”(实际上是“学习学习”量子态)的机器学习子领域,不需要对要估计的状态形式进行任何分析。尽管有这种普遍性,但它可以在几小时内在一台笔记本电脑上重新训练,用于两量子位的情况,这表明当扩展到更大的系统时,这是可行的时间成本,如果提供额外的结构,例如状态 ansatz,则可能会提高速度。不需要任何关于要估计的状态形式的 ansatz。尽管有这种普遍性,但它可以在几小时内在一台笔记本电脑上重新训练,用于两量子位的情况,这表明当扩展到更大的系统时,这是可行的时间成本,如果提供额外的结构,例如状态 ansatz,则可能会提高速度。不需要任何关于要估计的状态形式的 ansatz。尽管有这种普遍性,但它可以在几小时内在一台笔记本电脑上重新训练,用于两量子位的情况,这表明当扩展到更大的系统时,这是可行的时间成本,如果提供额外的结构,例如状态 ansatz,则可能会提高速度。

更新日期:2021-06-24
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