论文标题
神经网络量子层析成像中的两倍实验
Neural network quantum state tomography in a two-qubit experiment
论文作者
论文摘要
我们使用两光子实验的测量数据研究了基于神经网络量子状态的有效量子状态层析成像方法的性能。机器学习启发的变异方法为量子模拟器的可扩展状态表征提供了有希望的途径。尽管这些方法的功能已在合成数据上证明，但实际实验数据的应用仍然很少。我们通过将它们应用于产生两个Qubit纠缠状态的实验的测量数据来进行基准和比较几种此类方法。我们发现，在存在实验性缺陷和噪声的情况下，将变异流形限制在物理状态下，即呈阳性的半明确密度矩阵，极大地提高了重建状态的质量，但使学习过程更加要求。包括额外的，可能是不合理的约束，例如假设纯状态，促进了学习，但也会偏向估计量。
We study the performance of efficient quantum state tomography methods based on neural network quantum states using measured data from a two-photon experiment. Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states, i.e. to positive semi-definite density matrices, greatly improves the quality of the reconstructed states but renders the learning procedure more demanding. Including additional, possibly unjustified, constraints, such as assuming pure states, facilitates learning, but also biases the estimator.
