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AI detector for superconducting qubit charge jumps achieves real-time control

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.

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AI detector for superconducting qubit charge jumps achieves real-time control

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Daniel Gaytan-Villarreal, Peter Meiring, Daniel Baxter, Daniel Bowring, Grace Bratrud, Matteo Cremonesi, Giuseppe Di Guglielmo, Grace Wagner, Bowen Xiao ·

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

    arXiv:2607.14293v1 Announce Type: cross Abstract: 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 si…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    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 q…