Robust and fast post-processing of single-shot spin qubit detection events with a neural network

Area of Science:

• Quantum Computing
• Machine Learning

Background:

• Efficient qubit readout is vital for quantum error correction.
• Single-shot spin detection events are key qubit measurement signals.

Purpose of the Study:

• To evaluate neural networks for classifying qubit readout signals.
• To benchmark neural networks against Bayesian inference filters.

Main Methods:

• Testing neural networks trained on experimental and synthetic readout traces.
• Comparing neural network performance with a Bayesian inference filter.

Main Results:

• Networks trained on synthetic data match Bayesian filter performance in error and speed.
• Synthetic-trained networks show increased robustness to signal variations.
• A 7% increase in Rabi oscillation visibility was observed using synthetic-trained networks.

Conclusions:

• Neural networks trained on synthetic data are effective for qubit readout.
• These networks offer robustness and potential improvements in quantum processor architectures.