Machine Learning Enhanced Quantum State Tomography on FPGA
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913640530051072 |
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| author | 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 Lee, Ray-Kuang |
| author_facet | 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 Lee, Ray-Kuang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Enhanced Quantum State Tomography on FPGA 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 Lee, Ray-Kuang Quantum Physics 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. |
| title | Machine Learning Enhanced Quantum State Tomography on FPGA |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2501.04327 |