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arXiv 2009.07601quant-phcs.LG
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基于基依赖神经网络的高效量子态样品层析成像

Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks

Imperial College London（伦敦帝国理工学院）；California Institute of Technology（加州理工学院）

Alistair W. R. Smith, Johnnie Gray, M. S. Kim

发表信息：
PRX Quantum 2, 020348 (2021)
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摘要：

我们使用元学习神经网络方法来分析来自测量的量子态的数据。一旦我们的神经网络被训练好，它就可以用来有效地对训练数据中不包含的测量基中的状态测量进行采样。这些样本可用于计算期望值和其他有用的量。我们把这个过程称为“状态样本层析成像”。我们使用一个有效的参数化生成神经网络编码状态的测量结果分布。这使得即使对于大型系统，也可以有效地执行断层扫描过程中的每个阶段。我们的方案在最近的IBM Quantum设备上进行了演示，产生了一个6量子比特状态测量结果的模型，对于所有测试用例，预测准确度（经典保真度）> 95%，仅使用100个随机测量设置，而不是使用本地测量的标准全断层扫描所需的729个设置。所需测量次数的这种减少有利地缩放，200个测量设置中的训练数据对于10量子比特状态产生> 92%的预测准确度，其中对于基于完全局部测量的量子态断层扫描通常需要59，049个设置。在这种情况下，将测量次数减少近600倍可以允许在当前量子设备上的实际时间内估计期望值和状态不确定性。

英文摘要：

We use a meta-learning neural-network approach to analyse data from a measured quantum state. Once our neural network has been trained it can be used to efficiently sample measurements of the state in measurement bases not contained in the training data. These samples can be used calculate expectation values and other useful quantities. We refer to this process as "state sample tomography". We encode the state's measurement outcome distributions using an efficiently parameterized generative neural network. This allows each stage in the tomography process to be performed efficiently even for large systems. Our scheme is demonstrated on recent IBM Quantum devices, producing a model for a 6-qubit state's measurement outcomes with a predictive accuracy (classical fidelity) > 95% for all test cases using only 100 random measurement settings as opposed to the 729 settings required for standard full tomography using local measurements. This reduction in the required number of measurements scales favourably, with training data in 200 measurement settings yielding a predictive accuracy > 92% for a 10 qubit state where 59,049 settings are typically required for full local measurement-based quantum state tomography. A reduction in number of measurements by a factor, in this case, of almost 600 could allow for estimations of expectation values and state fidelities in practicable times on current quantum devices.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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