Peter Meiring | Searcharxiv

arXiv2026

Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network

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$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 \mu$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $\chi^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|\Delta q| \in [0.1, 0.5] e$ 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.

quant-ph↗

arXiv2026

DGNNFlow: A Streaming Dataflow Architecture for Real-Time Edge-based Dynamic GNN Inference in HL-LHC Trigger Systems

Dynamic GNN inference exhibits strong capability to model interactions over time, such as complex particle collision events in High Energy Physics (HEP) experiments at High Luminosity Large Hadron Collider (HL-LHC). With much larger scale of collision data captured in future HEP experiments to help unlocking physics discoveries and limitation in both offline compute capacity and storage, revamped trigger systems require FPGAs to run ultra-low-latency Machine Learning models with low power consumption for online filtering of useful events. Many state-of-the-art GNN accelerators relied on static graph structures, but this assumption breaks down in HL-LHC trigger systems and other edge-based dynamic GNN applications where edge embeddings can change in-place based on neighbor node embeddings during runtime. We propose DGNNFlow, a novel streaming dataflow architecture for real-time edge-based dynamic GNN inference applications (including but not limited to HL-LHC trigger systems) along with three key contributions. First, we introduce hardware enhancement for edge embedding dynamic computation. Second, we alleviate data dependencies in edge-based dynamic GNN dataflow with Node Embedding Broadcast. Third, we provide input dynamic graph construction for complete support of graphs without pre-defined edge embeddings. We deploy DGNNFlow using AMD Alveo U50 FPGA to evaluate performance at 200 MHz clock frequency. DGNNFlow achieved 2.59x-4.36x and 1.30x-2.14x speedup compared to NVIDIA RTX A6000 GPU (batch sizes 1 and 2) with 3.59x-3.70x less power consumption, achieved 2.29x-3.54x speedup with 1.93x-2.12x less power consumption compared to Intel Xeon Gold 6226R CPU. Our implementation is available on GitHub.

cs.DC↗