In accordance with the fact that quantum measurements are described in terms of positive operator measures (POMs), we consider certain aspects of a quantization scheme in which a classical variable $f:\R^2\to \R$ is associated with a unique positive operator measure (POM) $E^f$, which is not necessarily projection valued. The motivation for such a scheme comes from the well-known fact that due to the noise in a quantum measurement, the resulting outcome distribution is given by a POM and cannot, in general, be described in terms of a traditional observable, a selfadjoint operator. Accordingly, we notice that the noiseless measurements are the ones which are determined by a selfadjoint operator. The POM $E^f$ in our quantization is defined through its moment operators, which are required to be of the form $Γ(f^k)$, $k\in \N$, with $Γ$ a fixed map from classical variables to Hilbert space operators. In particular, we consider the quantization of classical \emph{questions}, that is, functions $f:\R^2\to\R$ taking only values 0 and 1. We compare two concrete realizations of the map $Γ$ in view of their ability to produce noiseless measurements: one being the Weyl map, and the other defined by using phase space probability distributions.
Asymmetry effects in homodyne and heterodyne measurements: Positive operator-valued measures and asymptotic security of Gaussian-continuous-variable quantum key distribution
ITMO University(伊托夫大学);Laboratory for Quantum Communications, ITMO University(伊托夫大学量子通信实验室);Laboratory of Quantum Processes and Measurements, ITMO University(伊托夫大学量子过程与测量实验室)
Constant-Depth Quantum Imaginary Time Evolution Using Dynamic Fan-out Circuits
Jeppesen ForeFlight(杰普森福飞);Department of Microtechnology and Nanoscience (MC2), Chalmers University of Technology(查尔姆斯理工大学微技术和纳米科学系(MC2));Department of Chemistry and Chemical Engineering, Chalmers University of Technology(查尔姆斯理工大学化学与化学工程系);Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf(德累斯顿-罗斯多夫亥姆霍兹中心高级系统理解中心);Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, TU Dresden(德累斯顿/莱比锡可扩展数据分析与人工智能中心,德累斯顿工业大学)