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Machine Learning Enhanced Quantum State Tomography on FPGA

Hsun-Chung Wu, Hsien-Yi Hsieh, Hua Li Chen, Zi-Hao Shi, Yi-Ru Chen, Te-Hwei Suen, Chien-Ming Wu, Ray‐Kuang Lee

Published Jan 1, 2025• 0 citations
Publisher

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.

DOI
10.1364/cleo_at.2025.jps200_81

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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.
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 on edge devices, specifically utilizing field-programmable gate arrays (FPGAs). Our implementation uses the Vitis AI Integrated Development Environment provided by AMD® Inc. Compared to graphics processing unit-based machine learning QST, our FPGA-based approach reduces the average inference time by an order of magnitude, from 38 to 2.94 ms, but only suffers an average fidelity reduction by about 1% (from 0.99 to 0.98). This FPGA-based QST system 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.
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.
Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to stringent timing constraints and algorithmic complexity, most qubit state discrimination methods are executed offline. In this work, we present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology. A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform, enabling accurate in-situ state discrimination and supporting mid-circuit measurement experiments for multiple qubits. Our approach leverages recent advancements in architecture research and design, utilizing specialized AI/ML accelerators to optimize quantum experiments and reduce the use of FPGA resources.
By using ZCU 104 Evaluation Board with the Vitis AI Integrated Development Environment, we have successfully deployed machine learning-based quantum state tomography onto a FPGA device, with a slight reduction in the output average fidelity, i.e., from 0.99 to 0.98. But the signal processing speed when reconstructing quantum state from tomography is demonstrated to be 10 times higher, taking 2.94 ms, instead 38 ms previously. With the flexibility and parallel processing abilities, our FPGA-based QST offers a highly efficient and precise tool toward real-time diagnostics of quantum states. In addition to application to Gaussian states, as illustrated here, this technology paves the way to dealing with more general quantum states, including non-Gaussian states and multi-partite quantum states at high throughput speeds.
With this talk, I will first illustrate the implementation of our machine-learning (ML) enhanced quantum state tomography (QST) for continuous variables, through the experimentally measured data generated from squeezed vacuum states, as an example of quantum machine learning. At the same time, as a collaborator for LIGO-VirgoKAGRA (LVK) gravitational wave network and Einstein Telescope, our plan to inject this squeezed vacuum field into the advanced gravitational wave detectors (GWD) will be introduced. Finally, I will report our recent progress in applying such a ML-QST as a crucial diagnostic toolbox for applications with squeezed states, from Wigner currents, optical cat state generation, and Bayesian estimation for GWD.
The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noisy quantum computers have, thereby limiting the number of features encoded for quantum kernels. Hence, there is a need for an efficient, application-specific simulator for quantum computing by using classical technology. Here we focus on quantum kernels empirically designed for image classification and demonstrate a field programmable gate arrays (FPGA) implementation. We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features, up to 780-dimensional features. We apply our quantum kernel to classification tasks using the Fashion-MNIST dataset and show that our quantum kernel is comparable to Gaussian kernels with the optimized hyperparameter.