Enhanced Measurement of Neutral Atom Qubits with Machine Learning

Abstract

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 5-layer single-qubit CNN architecture and a 6-layer multi-qubit CNN architecture. We benchmark both architectures against a conventional Gaussian threshold analysis method. In a sparse array (9 μm atom separation) which experiences negligible crosstalk, we observed up to 32% and 56% error reduction for the multi-qubit and single-qubit architectures respectively, as compared to the benchmark. In a tightly spaced array (5 μm atom separation), which suffers from readout crosstalk, we observed up to 43% and 32% error reduction in the multi-qubit and single-qubit CNN architectures respectively, as compared to the benchmark. By examining the correlation between the predicted states of neighboring qubits, we found that the multi-qubit CNN architecture reduces the crosstalk correlation 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.

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References in corpus (12)

• Suppressing quantum errors by scaling a surface code logical qubit
• Demonstration of multi-qubit entanglement and algorithms on a programmable neutral atom quantum computer
• High-fidelity parallel entangling gates on a neutral atom quantum computer
• Exponential suppression of bit or phase flip errors with repetitive error correction
• Mitigating measurement errors in multi-qubit experiments
• High-fidelity gates with mid-circuit erasure conversion in a metastable neutral atom qubit
• Mid-circuit correction of correlated phase errors using an array of spectator qubits
• Mid-circuit cavity measurement in a neutral atom array
• Parallel low-loss measurement of multiple atomic qubits
• Fast non-destructive parallel readout of neutral atom registers in optical potentials
• Randomized Benchmarking using Non-Destructive Readout in a 2D Atom Array
• An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes

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• Efficient measurement of neutral-atom qubits with matched filters
• Laser cooling and qubit measurements on a forbidden transition in neutral Cs atoms
• Asymmetry analysis of Autler-Townes doublet in the trap-loss fluorescence spectroscopy of cesium MOT with single step Rydberg excitation