Speaker
Description
Ionizing radiation from cosmic rays and ambient radioactivity induces charge jumps in superconducting qubits, causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, providing only a post-hoc record of charge events. By the time a jump is identified, the affected experiment is already over, and no adaptive response is possible. In this work, a real-time charge-jump detector for superconducting qubits, based on a dilated causal convolutional neural network (DC-CNN) 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 optimized fixed-point quantization, achieving a low, microsecond-scale per-inference latency on the Zynq UltraScale+ RFSoC ZCU216. At this operating point, the DC-CNN matches the detection efficiency of the established offline $\chi^2$ algorithm across a broad range of charge-jump magnitudes at a 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.
| Tutorial level (only for Tutorial) | Advanced |
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| Do you plan to submit a 4-page extended abstract on OpenReview (only for Presentations/Posters)? | Maybe |