Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network
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Abstract (arXiv)
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 apfixedlangle 16,6 rangle quantization, reaching a per-inference latency of 32.0 μs on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline chi2 algorithm (0.843 ± 0.022 vs. 0.868 ± 0.007 on |Delta q| ∈ [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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 39 references that are in the map. First posted on arXiv 15 Jul 2026.
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References · 7
- 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
- 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
- 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 78
- 2021ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Trainingcited by 61
- 2020A Statistical Framework for Low-bitwidth Training of Deep Neural Networkscited by 51
- 2025End-to-end workflow for machine learning-based qubit readout with QICK and hls4mlcited by 8
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Citing papers per year: 2026: 1.
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