Machine Learning Enhanced Quantum State Tomography on FPGA

• URL: http://arxiv.org/abs/2501.04327v1
• Date: Wed, 08 Jan 2025 07:59:40 GMT
• Title: Machine Learning Enhanced Quantum State Tomography on FPGA
• Authors: Hsun-Chung Wu, Hsien-Yi Hsieh, Zhi-Kai Xu, Hua Li Chen, Zi-Hao Shi, Po-Han Wang, Popo Yang, Ole Steuernagel, Chien-Ming Wu, Ray-Kuang Lee,
• Abstract summary: Machine learning techniques have opened new avenues for real-time quantum state tomography (QST) We demonstrate the deployment of machine learning-based QST onto edge devices, specifically utilizing field programmable gate arrays (FPGAs)
• Score: 0.12171407135748794
• License:
• 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.

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