Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network
Abstract
Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed⟨ 16,6 ⟩ quantization, reaching a per-inference latency of $ 32.0 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline χ2 algorithm (0.843 ± 0.022 vs. 0.868 ± 0.007 on |Δq| ∈ [0.1, 0.5] e at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.
Related
- Quantum convolutional neural network for predicting nuclear charge radii
- Experimental demonstration of the Quantum Fourier Transform on up to 100 qubits using a convolutional compilation strategy
- Tunable, modeless, and hybridization-free cross-Kerr coupler for miniaturized superconducting qubits
- Modeling Logical Gates and Readout of Superconducting Gottesman-Kitaev-Preskill Qubits
- Towards fault-tolerant quantum computing with optically controlled superconducting qubits
BibTeX
@misc{gaytanvillarreal2026realtime,
title = {{Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network}},
author = {Daniel Gaytan-Villarreal and Peter Meiring and Daniel Baxter and Daniel Bowring and Grace Bratrud and Matteo Cremonesi and Giuseppe Di Guglielmo and Grace Wagner and Bowen Xiao},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.14293},
eprint = {2607.14293},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.14293},
}
RIS
TY - UNPB
TI - Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network
AU - Daniel Gaytan-Villarreal
AU - Peter Meiring
AU - Daniel Baxter
AU - Daniel Bowring
AU - Grace Bratrud
AU - Matteo Cremonesi
AU - Giuseppe Di Guglielmo
AU - Grace Wagner
AU - Bowen Xiao
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2607.14293
UR - https://arxiv.org/abs/2607.14293
AB - Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed⟨ 16,6 ⟩ quantization, reaching a per-inference latency of $ 32.0 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline χ^2 algorithm (0.843 ± 0.022 vs. 0.868 ± 0.007 on |Δq| ∈ [0.1, 0.5] e at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.
ER -