Machine learning techniques have opened new avenues for real-time quantum state tomography (QST). In this work, we demonstrate the deployment of machine learning-based QST onto edge devices, specifically utilizing field programmable gate arrays (FPGAs). This implementation is realized using the {\it Vitis AI Integrated Development Environment} provided by AMD\textsuperscript \textregistered~Inc. Compared to the Graphics Processing Unit (GPU)-based machine learning QST, our FPGA-based one reduces the average inference time by an order of magnitude, from 38 ms to 2.94 ms, but only sacrifices the average fidelity about 1% reduction (from 0.99 to 0.98). The FPGA-based QST offers a highly efficient and precise tool for diagnosing quantum states, marking a significant advancement in the practical applications for quantum information processing and quantum sensing.
@article{arxiv.2501.04327,
title = {Machine Learning Enhanced Quantum State Tomography on FPGA},
author = {Hsun-Chung Wu and Hsien-Yi Hsieh and Zhi-Kai Xu and Hua Li Chen and Zi-Hao Shi and Po-Han Wang and Popo Yang and Ole Steuernagel and Chien-Ming Wu and Ray-Kuang Lee},
journal= {arXiv preprint arXiv:2501.04327},
year = {2025}
}