Enhanced measurement of neutral-atom qubits with machine learning

We demonstrate qubit-state measurements assisted by a supervised convolutional neural network (CNN) in a neutral-atom quantum processor. We present two CNN architectures for analyzing neutral-atom qubit readout data: a compact five-layer single-qubit CNN architecture and a six-layer multiqubit CNN architecture. We benchmark both architectures against a conventional Gaussian-threshold analysis method. In a sparse array (9-$\text{μ}\mathrm{m}$ atom separation) which experiences negligible crosstalk, we have observed up to 32% and 56% error reduction for the multiqubit and single-qubit architectures, respectively, as compared to the benchmark. In a tightly spaced array (5-$\text{μ}\mathrm{m}$ atom separation), which suffers from readout crosstalk, we have observed up to 43% and 32% error reduction in the multiqubit and single-qubit CNN architectures, respectively, as compared to the benchmark. By examining the correlation between the predicted states of neighboring qubits, we have found that the multiqubit CNN architecture reduces the crosstalk correlation by up to 78.5%. This work demonstrates a proof of concept for a CNN network to be implemented as a real-time readout-processing method on a neutral-atom quantum computer, enabling faster readout time and improved fidelity.