论文标题
高振幅状态的连续变化量子断层扫描
Continuous-variable quantum tomography of high-amplitude states
论文作者
论文摘要
量子层析成像是现代量子技术的重要组成部分。在应用于连续变量的谐波启动器系统（例如电磁场）中，现有的断层扫描方法通常在离散碱基中重建状态，因此仅限于具有相对较低振幅和能量的州。在这里，我们通过利用前馈神经网络在连续位置以直接获得密度矩阵来克服这一局限性。我们方法的一个重要好处是能够在相空间中选择特定区域进行详细重建。这会导致相对较慢的缩放量表，以状态幅度重建所需的资源量，因此使我们能够大大增加使用方法可访问的振幅范围。
Quantum state tomography is an essential component of modern quantum technology. In application to continuous-variable harmonic-oscilator systems, such as the electromagnetic field, existing tomography methods typically reconstruct the state in discrete bases, and are hence limited to states with relatively low amplitudes and energies. Here we overcome this limitation by utilizing a feed-forward neural network to obtain the density matrix directly in the continuous position basis. An important benefit of our approach is the ability to choose specific regions in the phase space for detailed reconstruction. This results in relatively slow scaling of the amount of resources required for the reconstruction with the state amplitude, and hence allows us to dramatically increase the range of amplitudes accessible with our method.
