mlsanityData to 7 Oct 2026

End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml

arXiv 2501.14663 ↗First posted 24 Jan 2025quant-ph cs.LGcited by 8

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Abstract (arXiv)

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC 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 APIs. 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 32ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 49 references that are in the map. First posted on arXiv 24 Jan 2025.

Builds on

14 of 49 references.

References · 14

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  2. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  3. 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 200
  4. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
  5. 2022How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projectionscited by 121
  6. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 78
  7. 2021ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Trainingcited by 61
  8. 2020A Statistical Framework for Low-bitwidth Training of Deep Neural Networkscited by 51
  9. 2022Assessing requirements to scale to practical quantum advantagecited by 10
  10. 2022Scaling Qubit Readout with Hardware Efficient Machine Learning Architecturescited by 6
  11. 2024Low-latency machine learning FPGA accelerator for multi-qubit-state discriminationcited by 3
  12. 2024Efficient and Scalable Architectures for Multi-Level Superconducting Qubit Readoutcited by 2
2 more
  1. 2024Reliable edge machine learning hardware for scientific applicationscited by 2
  2. 2022Enhancing Qubit Readout with Autoencoderscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardwarecited by 8
  2. 2025AI Benchmark Democratization and Carpentrycited by 3
  3. 2025An MLCommons Scientific Benchmarks Ontologycited by 2
  4. 2026Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Networkcited by 1
  5. 2025Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systemscited by 0
  6. 2025LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readoutcited by 0
  7. 2026Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov-Arnold Networkscited by 0
  8. 2026SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesigncited by 0