End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Di Guglielmo, Giuseppe Du, Botao Campos, Javier Boltasseva, Alexandra Dixit, Akash V. Fahim, Farah Kudyshev, Zhaxylyk Lopez, Santiago Ma, Ruichao Perdue, Gabriel N. Tran, Nhan Yesilyurt, Omer Bowring, Daniel |
| author_facet | Di Guglielmo, Giuseppe Du, Botao Campos, Javier Boltasseva, Alexandra Dixit, Akash V. Fahim, Farah Kudyshev, Zhaxylyk Lopez, Santiago Ma, Ruichao Perdue, Gabriel N. Tran, Nhan Yesilyurt, Omer Bowring, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml Di Guglielmo, Giuseppe Du, Botao Campos, Javier Boltasseva, Alexandra Dixit, Akash V. Fahim, Farah Kudyshev, Zhaxylyk Lopez, Santiago Ma, Ruichao Perdue, Gabriel N. Tran, Nhan Yesilyurt, Omer Bowring, Daniel Quantum Physics Machine Learning 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. |
| title | End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2501.14663 |