论文标题
神经网络增强了分子地面的测量效率
Neural network enhanced measurement efficiency for molecular groundstates
论文作者
论文摘要
据信，量子计算机的第一个有用应用之一将是制备分子汉密尔顿人的地面。涉及状态准备和读数的至关重要的任务是获得此类状态的物理可观察，通常是使用量子台上的投影测量值估算的。目前，测量数据是昂贵且耗时的，以便在任何量子计算体系结构上获得，这对估计器的统计误差产生了重大影响。在本文中，我们从典型的测量数据中适应了常见的神经网络模型（受限制的Boltzmann机器和经常性神经网络），以了解几种原型分子量子量汉顿量的复杂地面史塔特波函数。通过将重建的地面能量的准确性$ \ varepsilon $与测量数量相关联，我们发现，使用神经网络模型可以通过单独使用单拷贝测量结果来重建可观察到的可观察结果，从而提供了强大的改进。对于基于模型的方法，这种增强可产生$ \ varepsilon^{ - 1} $接近$ \ varepsilon^{ - 1} $的渐近缩放，而不是$ \ varepsilon^{ - 2} $在古典影子中的情况下。
It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state preparation and readout is obtaining physical observables of such states, which are typically estimated using projective measurements on the qubits. At present, measurement data is costly and time-consuming to obtain on any quantum computing architecture, which has significant consequences for the statistical errors of estimators. In this paper, we adapt common neural network models (restricted Boltzmann machines and recurrent neural networks) to learn complex groundstate wavefunctions for several prototypical molecular qubit Hamiltonians from typical measurement data. By relating the accuracy $\varepsilon$ of the reconstructed groundstate energy to the number of measurements, we find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables. This enhancement yields an asymptotic scaling near $\varepsilon^{-1}$ for the model-based approaches, as opposed to $\varepsilon^{-2}$ in the case of classical shadow tomography.
