论文标题
具有基础依赖性神经网络的有效量子状态层析成像
Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks
论文作者
论文摘要
我们使用元学习的神经网络方法来分析来自测得的量子状态的数据。一旦训练了我们的神经网络，它可用于在训练数据中未包含的测量基础中有效地对状态进行样品测量。可以使用这些样品计算预期值和其他有用的数量。我们将此过程称为“州样本断层扫描”。我们使用有效参数化的生成神经网络编码州的测量结果分布。这允许在大型系统中有效地执行层析成像过程中的每个阶段。我们的方案在最近的IBM量子设备上证明了这一方案，该模型为6 Quibent State的测量结果提供了预测精度（经典保真度）> 95％> 95％，仅使用100个随机测量设置，而不是使用局部测量的标准完整层造影所需的729个随机测量设置。所需的测量数量的减少量表非常有利，在200个测量设置中，训练数据可产生预测精度> 92％的10量子状态，其中通常需要59,049个基于局部测量的量子层表术，因此需要进行59,049个设置。在这种情况下，将测量数量减少了一个因素，可以估算到当前量子设备上可行的时间的期望值和状态保真度。
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.
