Enhanced Measurement of Neutral Atom Qubits with Machine Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910567124434944 |
|---|---|
| author | Phuttitarn, L. Becker, B. M. Chinnarasu, R. Graham, T. M. Saffman, M. |
| author_facet | Phuttitarn, L. Becker, B. M. Chinnarasu, R. Graham, T. M. Saffman, M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12217 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhanced Measurement of Neutral Atom Qubits with Machine Learning Phuttitarn, L. Becker, B. M. Chinnarasu, R. Graham, T. M. Saffman, M. Quantum Physics Atomic Physics 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. |
| title | Enhanced Measurement of Neutral Atom Qubits with Machine Learning |
| topic | Quantum Physics Atomic Physics |
| url | https://arxiv.org/abs/2311.12217 |