Speaker
Description
As a crucial diagnostic toolbox for applications with squeezed states, I will 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]. Then, I will demonstrate the deployment of ML-based QST onto edge devices, specifically utilizing Field-Programmable Gate Arrays (FPGAs) [2]. This implementation was realized using the ”Vitis AI” development environment provided by AMD Inc. The FPGA-based QST offers a highly efficient and precise tool for quantum noise reduction in advanced gravitational wave detectors.
[1] Hsien-Yi Hsieh, et al., "Extract the Degradation Information in Squeezed States with Machine Learning," Phys. Rev. Lett. 128, 073604 (2022).
[2] Hsun-Chung Wu, et al., "Machine learning enhanced quantum state tomography on a field-programmable gate array," APL Quantum 2, 026117 (2025); Cover; Featured Article; Scilight.
