Disruptive Information Processing Technologies Group, Raytheon BBN Technologies, Cambridge, Massachusetts 02138, USA.
Phys Rev Lett. 2011 Nov 18;107(21):210404. doi: 10.1103/PhysRevLett.107.210404. Epub 2011 Nov 16.
Quantum tomography is the main method used to assess the quality of quantum information processing devices. However, the amount of resources needed for quantum tomography is exponential in the device size. Part of the problem is that tomography generates much more information than is usually sought. Taking a more targeted approach, we develop schemes that enable (i) estimating the fidelity of an experiment to a theoretical ideal description, (ii) learning which description within a reduced subset best matches the experimental data. Both these approaches yield a significant reduction in resources compared to tomography. In particular, we demonstrate that fidelity can be estimated from a number of simple experiments that is independent of the system size, removing an important roadblock for the experimental study of larger quantum information processing units.
量子层析是评估量子信息处理设备质量的主要方法。然而,量子层析所需的资源量与设备的大小呈指数关系。部分问题在于层析会产生比通常所需更多的信息。通过采用更具针对性的方法,我们开发了一些方案,使(i)能够估计实验与理论理想描述的保真度,(ii)能够学习在简化的子集中哪个描述最符合实验数据。与层析相比,这两种方法都显著减少了资源需求。特别是,我们证明可以从数量独立于系统大小的简单实验中估计保真度,这为更大的量子信息处理单元的实验研究消除了一个重要的障碍。