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DOE OSTI · 2510763

End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml

Guglielmo, Giuseppe Di [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000257491432)·Du, Botao [Purdue Univ., West Lafayette, IN (United States)]·Campos, Javier [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)]·Boltasseva, Alexandra [Purdue Univ., West Lafayette, IN (United States)]·Dixit, Akash [National Inst. of Standards and Technology (NIST), Boulder, CO (United States)] (ORCID:0000000166997198)·Fahim, Farah [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000312521447)·Kudyshev, Zhaxylyk [Purdue Univ., West Lafayette, IN (United States)]·Lopez, Santiago [Purdue Univ., West Lafayette, IN (United States)]·Ma, Ruichao [Purdue Univ., West Lafayette, IN (United States)] (ORCID:000000034516874X)·Perdue, Gabriel N. [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000167858720)·Tran, Nhan [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000284406854)·Yesilyurt, Omer [Purdue Univ., West Lafayette, IN (United States)]·Bowring, Daniel [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000193802587)
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Abstract

In this article, we present an end-to-end workflow for superconducting qubit readout that embeds codesigned neural networks into the quantum instrumentation control kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx radiofrequency system-on-chip field-programmable gate arrays (FPGAs), we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python application programming interfaces. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA lookup table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

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42 ENGINEERINGMLRFSoCartificial neural networksfield programmable gate arrayshardwaremachine learningmicroprogrammingmicrowave amplifiersmicrowave circuitsquantum computingqubitradiofrequency system-on-chipresonant frequencysoftwaresuperconducting qubit readouttable lookup

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Guglielmo, Giuseppe Di [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000257491432), Du, Botao [Purdue Univ., West Lafayette, IN (United States)], Campos, Javier [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)], Boltasseva, Alexandra [Purdue Univ., West Lafayette, IN (United States)], Dixit, Akash [National Inst. of Standards and Technology (NIST), Boulder, CO (United States)] (ORCID:0000000166997198), Fahim, Farah [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000312521447), Kudyshev, Zhaxylyk [Purdue Univ., West Lafayette, IN (United States)], Lopez, Santiago [Purdue Univ., West Lafayette, IN (United States)], Ma, Ruichao [Purdue Univ., West Lafayette, IN (United States)] (ORCID:000000034516874X), Perdue, Gabriel N. [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000167858720), Tran, Nhan [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000284406854), Yesilyurt, Omer [Purdue Univ., West Lafayette, IN (United States)], Bowring, Daniel [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000193802587). 2025-01-01. End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml. https://doi.org/10.1109/tqe.2025.3604712

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