Machine Learning Enhanced Quantum State Tomography on FPGA

TL;DR

This paper demonstrates the deployment of machine learning-based quantum state tomography on FPGA edge devices, achieving faster inference times with minimal fidelity loss, advancing practical quantum information processing.

Contribution

It introduces a novel FPGA implementation of machine learning quantum state tomography, significantly reducing inference time compared to GPU-based methods.

Findings

Inference time reduced from 38 ms to 2.94 ms

Fidelity decreased by only 1% (from 0.99 to 0.98)

Enables real-time quantum state diagnosis on edge devices

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.