1. Machine Learning Enhanced Quantum State Tomography on FPGA
- Author
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Wu, Hsun-Chung, Hsieh, Hsien-Yi, Xu, Zhi-Kai, Chen, Hua Li, Shi, Zi-Hao, Wang, Po-Han, Yang, Popo, Steuernagel, Ole, Wu, Chien-Ming, and Lee, Ray-Kuang
- Subjects
Quantum Physics - Abstract
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., Comment: 6 pages, 5 figures
- Published
- 2025