Środowiskowe Seminarium z Informacji i Technologii Kwantowych

In this talk, I will first illustrate the implementation of our machine-learning (ML) enhanced quantum state tomography (QST) for continuous variables, through the experimentally measured data generated from squeezed vacuum states [1], as an example of quantum machine learning [2, 3]. Then, this ML-enhanced QST is also applied to the heralding single photon source, which has the second order correlation function, g2 < 0.04 [4]. Instead of ‘‘Schrodinger kitten’’ state generated by subtracting one photon from a squeezed vacuum beam, we report the first experimental realization of optical cat states by adding one photon to a squeezed vacuum state [5]. A variety of unique applications in linear optical quantum computing and quantum metrology will also be addressed [6].

[1] Hsien-Yi Hsieh, et al., "Extract the Degradation Information in Squeezed States with Machine Learning," Phys. Rev. Lett. 128, 073604 (2022).
[2] Hsien-Yi Hsieh, et al., "Direct parameter estimations from machine-learning enhanced quantum state tomography," Special Issue "Quantum Optimization & Machine Learning"; Symmetry 14, 874 (2022).
[3] Alexey Melnikov, Mohammad Kordzanganeh, Alexander Alodjants, and RKL," Quantum Machine Learning: from physics to software engineering," Adv. in Phys. X (Review Article) 8, 2165452 (2023).
[4] Yi-Ru, et al., "Machine-learning enhanced quantum state tomography for the heralding single photon source," (in preparation, 2023).
[5] Yi-Ru, et al., "Experimental Realization of Optical Cat States with Single-Photon-Added Squeezed States," arXiv: 2306.13011 (2023).
[6] Yuhang Zhao, et al., "Frequency-dependent squeezed vacuum source for broadband quantum noise reduction in advanced gravitational-wave detectors," Phys. Rev. Lett. 124, 171101 (2020); Editors' Suggestion; Featured in Physics.