Researchers have developed a novel online detector for charge jumps in superconducting qubits, utilizing a dilated causal convolutional neural network (DCCNN). This new method, deployable on the Quantum Instrumentation Control Kit (QICK) platform, significantly reduces latency compared to existing offline detection techniques. Trained on data from Fermilab's Northwestern Experimental Underground Site (NEXUS), the DCCNN achieves a per-inference latency of 6.19 μs and matches the detection efficiency of traditional methods, enabling real-time error mitigation and novel quantum sensing applications. AI
IMPACT Enables real-time control and error mitigation in quantum computing and sensing.
RANK_REASON Research paper detailing a new AI model for a specific scientific application.
Read on Hugging Face Daily Papers →
- arXiv
- Daniel Gaytan-Villarreal
- Fermilab
- Northwestern Experimental Underground Site (NEXUS)
- Quantum Instrumentation Control Kit (QICK)
- Zynq UltraScale+ RFSoC ZCU216
- DCCNN
- hls4ml
- Hugging Face Daily Papers
- Ramsey tomography
- superconducting qubits
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