Quantum readout error mitigation via deep learning
Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circ...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022-01-01
|
| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ac7b3d |
| _version_ | 1797748421539397632 |
|---|---|
| author | Jihye Kim Byungdu Oh Yonuk Chong Euyheon Hwang Daniel K Park |
| author_facet | Jihye Kim Byungdu Oh Yonuk Chong Euyheon Hwang Daniel K Park |
| author_sort | Jihye Kim |
| collection | DOAJ |
| description | Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network (NN) with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the NN and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices. |
| first_indexed | 2024-03-12T16:04:30Z |
| format | Article |
| id | doaj.art-54bf6c590f844a64a76d33a03ee0f0c2 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:04:30Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-54bf6c590f844a64a76d33a03ee0f0c22023-08-09T14:25:41ZengIOP PublishingNew Journal of Physics1367-26302022-01-0124707300910.1088/1367-2630/ac7b3dQuantum readout error mitigation via deep learningJihye Kim0Byungdu Oh1https://orcid.org/0000-0002-2709-3177Yonuk Chong2Euyheon Hwang3Daniel K Park4https://orcid.org/0000-0002-3177-4143SKKU Advanced Institute of Nanotechnology and Department of Nano Engineering , Suwon, 16419, Republic of KoreaSKKU Advanced Institute of Nanotechnology and Department of Nano Engineering , Suwon, 16419, Republic of KoreaSKKU Advanced Institute of Nanotechnology and Department of Nano Engineering , Suwon, 16419, Republic of KoreaSKKU Advanced Institute of Nanotechnology and Department of Nano Engineering , Suwon, 16419, Republic of KoreaDepartment of Applied Statistics, Yonsei University , Seoul, 03722, Republic of Korea; Department of Statistics and Data Science, Yonsei University , Seoul, 03722, Republic of KoreaQuantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network (NN) with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the NN and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices.https://doi.org/10.1088/1367-2630/ac7b3dquantum computingerror mitigationquantum controldeep learning |
| spellingShingle | Jihye Kim Byungdu Oh Yonuk Chong Euyheon Hwang Daniel K Park Quantum readout error mitigation via deep learning New Journal of Physics quantum computing error mitigation quantum control deep learning |
| title | Quantum readout error mitigation via deep learning |
| title_full | Quantum readout error mitigation via deep learning |
| title_fullStr | Quantum readout error mitigation via deep learning |
| title_full_unstemmed | Quantum readout error mitigation via deep learning |
| title_short | Quantum readout error mitigation via deep learning |
| title_sort | quantum readout error mitigation via deep learning |
| topic | quantum computing error mitigation quantum control deep learning |
| url | https://doi.org/10.1088/1367-2630/ac7b3d |
| work_keys_str_mv | AT jihyekim quantumreadouterrormitigationviadeeplearning AT byungduoh quantumreadouterrormitigationviadeeplearning AT yonukchong quantumreadouterrormitigationviadeeplearning AT euyheonhwang quantumreadouterrormitigationviadeeplearning AT danielkpark quantumreadouterrormitigationviadeeplearning |