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

arXiv: https://arxiv.org/abs/2501.14663 · HTML: https://mlsanity.com/paper/2501.14663/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections](https://mlsanity.com/paper/2206.12037/) (2022)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training](https://mlsanity.com/paper/2104.14129/) (2021)
- [A Statistical Framework for Low-bitwidth Training of Deep Neural Networks](https://mlsanity.com/paper/2010.14298/) (2020)
- [Assessing requirements to scale to practical quantum advantage](https://mlsanity.com/paper/2211.07629/) (2022)
- [Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures](https://mlsanity.com/paper/2212.03895/) (2022)
- [Low-latency machine learning FPGA accelerator for multi-qubit-state discrimination](https://mlsanity.com/paper/2407.03852/) (2024)
- [Efficient and Scalable Architectures for Multi-Level Superconducting Qubit Readout](https://mlsanity.com/paper/2405.08982/) (2024)
- [Reliable edge machine learning hardware for scientific applications](https://mlsanity.com/paper/2406.19522/) (2024)
- [Enhancing Qubit Readout with Autoencoders](https://mlsanity.com/paper/2212.00080/) (2022)

## Built on by (most cited first, 8 of 8)

- [hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware](https://mlsanity.com/paper/2512.01463/) (2025)
- [AI Benchmark Democratization and Carpentry](https://mlsanity.com/paper/2512.11588/) (2025)
- [An MLCommons Scientific Benchmarks Ontology](https://mlsanity.com/paper/2511.05614/) (2025)
- [Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network](https://mlsanity.com/paper/2607.14293/) (2026)
- [Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems](https://mlsanity.com/paper/2507.19205/) (2025)
- [LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout](https://mlsanity.com/paper/2512.07808/) (2025)
- [Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2602.02056/) (2026)
- [SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign](https://mlsanity.com/paper/2605.16138/) (2026)

## 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.