AI model detects qubit charge jumps in real-time for quantum computing

Researchers have developed a real-time detector for charge jumps in superconducting qubits using a dilated causal convolutional neural network (DCCNN). This new method, designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, significantly reduces latency compared to existing offline detection techniques. The DCCNN achieves a per-inference latency of 6.19 μs and demonstrates detection efficiency comparable to the established offline χ² algorithm, without requiring per-qubit hyperparameter tuning. This advancement enables adaptive protocols that can respond to radiation-induced events in situ, benefiting both fault-tolerant quantum computing error mitigation and quantum sensing applications. AI

IMPACT Enables real-time error mitigation and enhanced quantum sensing capabilities by integrating AI into quantum control loops.

RANK_REASON Academic paper detailing a novel application of AI for scientific research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Daniel Gaytan-Villarreal
• Fermilab
• Northwestern Experimental Underground Site (NEXUS)
• Quantum Instrumentation Control Kit (QICK)
• Zynq UltraScale+ RFSoC ZCU216

• paper
• infra

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