Quantum Process Tomography

Mário Ziman

Ze Wu, S. Li, Wenqiang Zheng +2 more

The essential tool to characterize dynamics of an open quantum system is quantum process tomography (QPT). Although standard QPT methods are hard to be scalable, simplified QPT approach is available if we have the prior knowledge that the system Hamiltonian commutes with the system-environment interaction Hamiltonian. Using a nuclear magnetic resonance (NMR) quantum simulator, we experimentally simulate dephasing channels to demonstrate the simplified QPT as well as the standard QPT method as a comparison. The experimental results agree well with our predictions which confirm the validity and better efficiency of the simplified QPT.

Norman Y. Yao, Chris R. Laumann, Alexey Vyacheslavovich Gorshkov +2 more

Lynden K. Shalm, Morgan W. Mitchell, Aephraim M. Steinberg

Quantum process tomography is often cited as providing all the information that can be known about a given quantum process. In this paper we have shown that even if two processes appear identical under process tomography, it is possible to distinguish them using an interferometric setup. Using this setup, it is possible to gain more information about a process than just tomography provides.

Péter Wittek

Xiaohu Yuan, Huaping Liu, WU Rebin +1 more

Quantum Process Tomography (QPT) is one of the most important task of checking the functionality of quantum information processing devices. However, there is a significant difficulty that the required resources grow scaling exponentially with the number of qubits during the QPT. Recently, a compressed sensing QPT (CS QPT) is proposed that can reduce the required resource significantly. But the work did not consider the outlier noises caused by the disturbance of the system or channel. In this paper, we propose a joint sparse coding QPT aiming for the reconstruction of the two or more similar quantum channel corrupted by sparse outlier noises simultaneously. The simulations verify that the method is robust to outlier noises and the obtained reconstruction matrix is superior to the QPT using compressed sensing method.