11th RQC Seminar
Prof. Anton Frisk Kockum
(Department of Microtechnology and Nanoscience, Chalmers University of Technology.）
ハイブリッド（Zoom ・ 理研 和光事業所 本部棟2階大会議室）
Quantum state and process tomography with machine learning and gradient descent
The ability to quickly and accurately characterise quantum states and dynamics is crucial for the development of quantum technologies. However, the problem of learning a general quantum state or process has exponential complexity in the size of the quantum system. In this talk, I will present some recent progress we have made for both quantum state and process tomography. For state tomography, I will show how generative adversarial neural networks can outperform standard methods in terms of both the amount of time and data needed [1,2]. For process tomography, I will show how optimization using constrained gradient descent can work both for instances with little data and for larger systems, regimes which previously required two different methods .
 S. Ahmed et al., Phys. Rev. Lett. 127, 140502 (2021)
 S. Ahmed et al., Phys. Rev. Res. 3, 033278 (2021)
 S. Ahmed et al., in preparation.