Noise-robust classification of single-shot electron spin readouts using a deep neural network
Abstract Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-09-01
|
| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-021-00470-7 |
| _version_ | 1818428383078383616 |
|---|---|
| author | Yuta Matsumoto Takafumi Fujita Arne Ludwig Andreas D. Wieck Kazunori Komatani Akira Oiwa |
| author_facet | Yuta Matsumoto Takafumi Fujita Arne Ludwig Andreas D. Wieck Kazunori Komatani Akira Oiwa |
| author_sort | Yuta Matsumoto |
| collection | DOAJ |
| description | Abstract Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise ratio, system temperature, and numerical parameters such as threshold values. Accurate charge sensing schemes that are robust under noisy environments are indispensable for developing a scalable fault-tolerant quantum computation architecture. In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN). Importantly, the DNN classifier is automatically configured for spin-up and spin-down traces in any noise environment by tuning the trainable parameters using the datasets of charge transition signals experimentally obtained at a charging line. Moreover, we verify that our DNN classification is robust under noisy environment in comparison to the two conventional classification methods used for charge and spin state measurements in various quantum dot experiments. |
| first_indexed | 2024-12-14T15:00:45Z |
| format | Article |
| id | doaj.art-e073c3ef709c42d6bdcb1d1cbc4dd41d |
| institution | Directory Open Access Journal |
| issn | 2056-6387 |
| language | English |
| last_indexed | 2024-12-14T15:00:45Z |
| publishDate | 2021-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj.art-e073c3ef709c42d6bdcb1d1cbc4dd41d2022-12-21T22:56:51ZengNature Portfolionpj Quantum Information2056-63872021-09-01711710.1038/s41534-021-00470-7Noise-robust classification of single-shot electron spin readouts using a deep neural networkYuta Matsumoto0Takafumi Fujita1Arne Ludwig2Andreas D. Wieck3Kazunori Komatani4Akira Oiwa5SANKEN (The Institute of Scientific and Industrial Research), Osaka UniversitySANKEN (The Institute of Scientific and Industrial Research), Osaka UniversityLehrstuhl für Angewandte Festkörperphysik, Ruhr-Universität BochumLehrstuhl für Angewandte Festkörperphysik, Ruhr-Universität BochumSANKEN (The Institute of Scientific and Industrial Research), Osaka UniversitySANKEN (The Institute of Scientific and Industrial Research), Osaka UniversityAbstract Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise ratio, system temperature, and numerical parameters such as threshold values. Accurate charge sensing schemes that are robust under noisy environments are indispensable for developing a scalable fault-tolerant quantum computation architecture. In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN). Importantly, the DNN classifier is automatically configured for spin-up and spin-down traces in any noise environment by tuning the trainable parameters using the datasets of charge transition signals experimentally obtained at a charging line. Moreover, we verify that our DNN classification is robust under noisy environment in comparison to the two conventional classification methods used for charge and spin state measurements in various quantum dot experiments.https://doi.org/10.1038/s41534-021-00470-7 |
| spellingShingle | Yuta Matsumoto Takafumi Fujita Arne Ludwig Andreas D. Wieck Kazunori Komatani Akira Oiwa Noise-robust classification of single-shot electron spin readouts using a deep neural network npj Quantum Information |
| title | Noise-robust classification of single-shot electron spin readouts using a deep neural network |
| title_full | Noise-robust classification of single-shot electron spin readouts using a deep neural network |
| title_fullStr | Noise-robust classification of single-shot electron spin readouts using a deep neural network |
| title_full_unstemmed | Noise-robust classification of single-shot electron spin readouts using a deep neural network |
| title_short | Noise-robust classification of single-shot electron spin readouts using a deep neural network |
| title_sort | noise robust classification of single shot electron spin readouts using a deep neural network |
| url | https://doi.org/10.1038/s41534-021-00470-7 |
| work_keys_str_mv | AT yutamatsumoto noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT takafumifujita noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT arneludwig noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT andreasdwieck noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT kazunorikomatani noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT akiraoiwa noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork |