Spiking neural networks for streaming qubit readout
A preprint proposes using spiking neural networks to perform qubit-state assignment from frequency-multiplexed readout signals in superconducting quantum processors. The method is designed to handle streaming measured traces that may contain crosstalk, qubit-state relaxation events, and other transient nonidealities. It targets faster and more accurate assignment for feedback, calibration, and error correction.
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What this could mean
- 0–2 yearsPlausible
Spiking neural network classifiers could be deployed on FPGA-based readout controllers in superconducting systems within two years, tightening the feedback loop for error correction by processing multiplexed traces as they stream.
Spiking neural networks are event-driven and compatible with low-latency edge hardware already used in qubit control and readout. The main precondition is showing that SNN assignment accuracy matches conventional neural networks on real multiplexed data, which the preprint appears to address.
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