Enhanced Measurement of Neutral Atom Qubits with Machine Learning
| Autor: | Phuttitarn, L., Becker, B. M., Chinnarasu, R., Graham, T. M., Saffman, M. |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1103/PhysRevApplied.22.024011 |
| Popis: | 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 {\mu}m 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 {\mu}m 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. Comment: additional data added |
| Databáze: | arXiv |
| Externí odkaz: |
|