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September 19, 2025

Machine Learning Enhanced Quantum State Tomography on FPGA

Key Points

The FPGA-based QST provides a precise and efficient tool for diagnosing quantum states, enhancing the application of machine learning.
Utilizing the Vitis AI development environment, this work demonstrates the integration of machine learning techniques into QST.
Real-time quantum state tomography is achieved through the deployment of efficient computational methods on edge devices like FPGA.
This advancement highlights a significant step forward in the practical application of quantum state tomography for quantum computing.

Abstract

The integration of machine learning techniques has 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 was realized using the ”Vitis AI” development environment provided by AMD Inc. The FPGA-based QST offers a highly efficient and precise tool for diagnosing quantum states, marking a significant advancement in the practical application of quantum state tomography.

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